{"cells":[{"cell_type":"markdown","source":["# Initialization"],"metadata":{"id":"AkJLjiImWmLR"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"aTYTwhGW1rZS","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711918467836,"user_tz":-120,"elapsed":79828,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"48849efd-8ee2-4456-c8b8-b021dc251e31"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["# connect to drive\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"ulPH65OC3gbh","executionInfo":{"status":"ok","timestamp":1711918470173,"user_tz":-120,"elapsed":2342,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}}},"outputs":[],"source":["# import\n","import os\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import numpy as np\n","\n","# libraries\n","from datetime import datetime\n","import re\n","%matplotlib inline\n","import numpy as np\n","import pandas as pd\n","import scipy\n","from datetime import datetime, timedelta\n","import statsmodels.api as sm\n","from statsmodels.tsa.api import VAR\n","import matplotlib.pyplot as plt\n","import pandas_datareader as pdr"]},{"cell_type":"markdown","source":["# Read row data, arrange, observe descriptive statistics"],"metadata":{"id":"a0vt5b8XXAg9"}},{"cell_type":"code","execution_count":3,"metadata":{"id":"-yxFdjSl2W-u","executionInfo":{"status":"ok","timestamp":1711918471165,"user_tz":-120,"elapsed":997,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}}},"outputs":[],"source":["# read data from google drive\n","data = pd.read_excel('/content/drive/MyDrive/Rady_for_R_GBPUSD_EPU.xlsx', parse_dates =['Date'])"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":261},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1711918471166,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-120},"id":"R0fAcGOg4LBv","outputId":"9a8e7f8c-1fb5-4042-d2f2-5e88effcd588"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Date    USDGBP  CPI_US  CPI_UK  Money Market Rate_US  \\\n","0 2000-01-01  1.639417   164.1  71.936                0.0604   \n","1 2000-02-01  1.599864   164.4  72.159                0.0610   \n","2 2000-03-01  1.580220   164.7  72.338                0.0620   \n","3 2000-04-01  1.583365   164.7  72.573                0.0631   \n","4 2000-05-01  1.508717   164.8  72.771                0.0676   \n","\n","   Money Market Rate_UK   IPI_US  IPI_UK   M2_US     M2_UK  Stock Market News  \\\n","0                0.0614  94.5458   112.5  4656.2  890517.0           0.258237   \n","1                0.0624  94.8185   113.7  4669.4  873293.0           0.259493   \n","2                0.0623  95.1983   113.6  4699.9  887465.0           0.262109   \n","3                0.0630  95.8921   113.2  4755.9  870743.0           0.259771   \n","4                0.0630  96.0691   113.6  4743.9  845815.0           0.257433   \n","\n","   Economic Development News  FED News  Micro Finance News  \\\n","0                   0.265871  0.255893            0.249303   \n","1                   0.261406  0.255016            0.252066   \n","2                   0.258737  0.254687            0.247164   \n","3                   0.263898  0.254085            0.248209   \n","4                   0.269059  0.253482            0.249254   \n","\n","   International Trade News      EPU_US      EPU_UK  \n","0                  0.260273  187.453420   53.748451  \n","1                  0.260401  108.823551   58.396511  \n","2                  0.265140   58.469634   61.960393  \n","3                  0.262308   92.103565  114.010429  \n","4                  0.259477  181.052572   46.368325  "],"text/html":["\n","  <div id=\"df-38bd07a8-33ac-4328-b839-e93831deb84a\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Date</th>\n","      <th>USDGBP</th>\n","      <th>CPI_US</th>\n","      <th>CPI_UK</th>\n","      <th>Money Market Rate_US</th>\n","      <th>Money Market Rate_UK</th>\n","      <th>IPI_US</th>\n","      <th>IPI_UK</th>\n","      <th>M2_US</th>\n","      <th>M2_UK</th>\n","      <th>Stock Market News</th>\n","      <th>Economic Development News</th>\n","      <th>FED News</th>\n","      <th>Micro Finance News</th>\n","      <th>International Trade News</th>\n","      <th>EPU_US</th>\n","      <th>EPU_UK</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2000-01-01</td>\n","      <td>1.639417</td>\n","      <td>164.1</td>\n","      <td>71.936</td>\n","      <td>0.0604</td>\n","      <td>0.0614</td>\n","      <td>94.5458</td>\n","      <td>112.5</td>\n","      <td>4656.2</td>\n","      <td>890517.0</td>\n","      <td>0.258237</td>\n","      <td>0.265871</td>\n","      <td>0.255893</td>\n","      <td>0.249303</td>\n","      <td>0.260273</td>\n","      <td>187.453420</td>\n","      <td>53.748451</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2000-02-01</td>\n","      <td>1.599864</td>\n","      <td>164.4</td>\n","      <td>72.159</td>\n","      <td>0.0610</td>\n","      <td>0.0624</td>\n","      <td>94.8185</td>\n","      <td>113.7</td>\n","      <td>4669.4</td>\n","      <td>873293.0</td>\n","      <td>0.259493</td>\n","      <td>0.261406</td>\n","      <td>0.255016</td>\n","      <td>0.252066</td>\n","      <td>0.260401</td>\n","      <td>108.823551</td>\n","      <td>58.396511</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2000-03-01</td>\n","      <td>1.580220</td>\n","      <td>164.7</td>\n","      <td>72.338</td>\n","      <td>0.0620</td>\n","      <td>0.0623</td>\n","      <td>95.1983</td>\n","      <td>113.6</td>\n","      <td>4699.9</td>\n","      <td>887465.0</td>\n","      <td>0.262109</td>\n","      <td>0.258737</td>\n","      <td>0.254687</td>\n","      <td>0.247164</td>\n","      <td>0.265140</td>\n","      <td>58.469634</td>\n","      <td>61.960393</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2000-04-01</td>\n","      <td>1.583365</td>\n","      <td>164.7</td>\n","      <td>72.573</td>\n","      <td>0.0631</td>\n","      <td>0.0630</td>\n","      <td>95.8921</td>\n","      <td>113.2</td>\n","      <td>4755.9</td>\n","      <td>870743.0</td>\n","      <td>0.259771</td>\n","      <td>0.263898</td>\n","      <td>0.254085</td>\n","      <td>0.248209</td>\n","      <td>0.262308</td>\n","      <td>92.103565</td>\n","      <td>114.010429</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2000-05-01</td>\n","      <td>1.508717</td>\n","      <td>164.8</td>\n","      <td>72.771</td>\n","      <td>0.0676</td>\n","      <td>0.0630</td>\n","      <td>96.0691</td>\n","      <td>113.6</td>\n","      <td>4743.9</td>\n","      <td>845815.0</td>\n","      <td>0.257433</td>\n","      <td>0.269059</td>\n","      <td>0.253482</td>\n","      <td>0.249254</td>\n","      <td>0.259477</td>\n","      <td>181.052572</td>\n","      <td>46.368325</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38bd07a8-33ac-4328-b839-e93831deb84a')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-38bd07a8-33ac-4328-b839-e93831deb84a button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-38bd07a8-33ac-4328-b839-e93831deb84a');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-4d59e78e-519e-4147-ace7-0957ff851ee4\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d59e78e-519e-4147-ace7-0957ff851ee4')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-4d59e78e-519e-4147-ace7-0957ff851ee4 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"data","summary":"{\n  \"name\": \"data\",\n  \"rows\": 212,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2000-01-01 00:00:00\",\n        \"max\": \"2017-08-01 00:00:00\",\n        \"num_unique_values\": 212,\n        \"samples\": [\n          \"2002-07-01 00:00:00\",\n          \"2014-06-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"USDGBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19095936557419393,\n        \"min\": 1.2329261904761906,\n        \"max\": 2.0700818181818184,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          1.5546413043478267,\n          1.6905785714285717,\n          1.5782704545454544\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.659319800751,\n        \"min\": 164.1,\n        \"max\": 239.842,\n        \"num_unique_values\": 202,\n        \"samples\": [\n          207.603,\n          170.9,\n          178.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.400193450826142,\n        \"min\": 71.936,\n        \"max\": 103.785,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          74.372,\n          100.203,\n          94.413\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020266283015506273,\n        \"min\": 0.0023,\n        \"max\": 0.0679,\n        \"num_unique_values\": 138,\n        \"samples\": [\n          0.022799999999999997,\n          0.0051,\n          0.057\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02248998379598572,\n        \"min\": 0.0028000000000000004,\n        \"max\": 0.0665,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          0.006500000000000001,\n          0.055700000000000006,\n          0.008100000000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.89908267386627,\n        \"min\": 87.065,\n        \"max\": 106.6134,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          93.5461,\n          105.4698,\n          97.6288\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.695325086932039,\n        \"min\": 95.2,\n        \"max\": 114.3,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          100.4,\n          113.8,\n          114.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2587.5775087515417,\n        \"min\": 4656.2,\n        \"max\": 13664.4,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          5574.9,\n          11370.1,\n          9524.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 710401.8471653981,\n        \"min\": 847.26,\n        \"max\": 2498761.0,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          1059303.0,\n          2498761.0,\n          1953363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Market News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002908404647966463,\n        \"min\": 0.2484262,\n        \"max\": 0.2742658,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.2603932,\n          0.2580105,\n          0.2544306\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economic Development News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0028177243343392687,\n        \"min\": 0.251880854,\n        \"max\": 0.269058652,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.257455101,\n          0.262130912,\n          0.261736333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FED News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0032532151144483872,\n        \"min\": 0.2490762,\n        \"max\": 0.2655337,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.2521258,\n          0.2577743,\n          0.2585963\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro Finance News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0056449680318106,\n        \"min\": 0.244891,\n        \"max\": 0.2725514,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.2555874,\n          0.2530432,\n          0.2512611\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"International Trade News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0032970366533118457,\n        \"min\": 0.2502086,\n        \"max\": 0.2689798,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.2643084,\n          0.2559477,\n          0.2559856\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.0760361904917,\n        \"min\": 31.95825829080659,\n        \"max\": 692.2387019062214,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          104.45757657507053,\n          229.16993284336283,\n          185.51310125526527\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 70.29593690168772,\n        \"min\": 24.0359419445406,\n        \"max\": 558.2236420188227,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          88.47257142994314,\n          68.77737113506677,\n          153.32764352624784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":4}],"source":["data.head()"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"ztN6xB0WBFU1","executionInfo":{"status":"ok","timestamp":1711918471167,"user_tz":-120,"elapsed":11,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}}},"outputs":[],"source":["data['Date'] = pd.to_datetime(data['Date'])\n","data['USDGBP'] = 1/data['USDGBP']\n","data.rename(columns={'USDGBP':'GBPUSD'}, inplace=True)\n","#data.info()"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":510},"executionInfo":{"elapsed":527,"status":"ok","timestamp":1711918471684,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-120},"id":"e-ebZQLCCXc8","outputId":"a6c5c8e4-3627-4f76-e05c-df702900cf6e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              GBPUSD   CPI_US   CPI_UK  Money Market Rate_US  \\\n","Date                                                           \n","2000-01-01  0.609973  164.100   71.936                0.0604   \n","2000-02-01  0.625053  164.400   72.159                0.0610   \n","2000-03-01  0.632823  164.700   72.338                0.0620   \n","2000-04-01  0.631566  164.700   72.573                0.0631   \n","2000-05-01  0.662815  164.800   72.771                0.0676   \n","...              ...      ...      ...                   ...   \n","2017-04-01  0.791045  237.808  102.949                0.0116   \n","2017-05-01  0.773478  238.078  103.309                0.0119   \n","2017-06-01  0.780442  238.908  103.284                0.0126   \n","2017-07-01  0.769593  239.362  103.215                0.0131   \n","2017-08-01  0.771999  239.842  103.785                0.0131   \n","\n","            Money Market Rate_UK    IPI_US  IPI_UK    M2_US      M2_UK  \\\n","Date                                                                     \n","2000-01-01                0.0614   94.5458   112.5   4656.2   890517.0   \n","2000-02-01                0.0624   94.8185   113.7   4669.4   873293.0   \n","2000-03-01                0.0623   95.1983   113.6   4699.9   887465.0   \n","2000-04-01                0.0630   95.8921   113.2   4755.9   870743.0   \n","2000-05-01                0.0630   96.0691   113.6   4743.9   845815.0   \n","...                          ...       ...     ...      ...        ...   \n","2017-04-01                0.0033  105.0468   101.8  13452.7  2133785.0   \n","2017-05-01                0.0031  105.0136   102.0  13517.8  2128826.0   \n","2017-06-01                0.0029  105.2469   102.4  13544.0  2161166.0   \n","2017-07-01                0.0029  105.1038   102.8  13620.4  2195459.0   \n","2017-08-01                0.0028  104.3403   102.9  13664.4  2150923.0   \n","\n","            Stock Market News  Economic Development News  FED News  \\\n","Date                                                                 \n","2000-01-01           0.258237                   0.265871  0.255893   \n","2000-02-01           0.259493                   0.261406  0.255016   \n","2000-03-01           0.262109                   0.258737  0.254687   \n","2000-04-01           0.259771                   0.263898  0.254085   \n","2000-05-01           0.257433                   0.269059  0.253482   \n","...                       ...                        ...       ...   \n","2017-04-01           0.255181                   0.257543  0.260854   \n","2017-05-01           0.255313                   0.251881  0.256059   \n","2017-06-01           0.255768                   0.259531  0.264680   \n","2017-07-01           0.255551                   0.258921  0.262572   \n","2017-08-01           0.254705                   0.256595  0.260185   \n","\n","            Micro Finance News  International Trade News      EPU_US  \\\n","Date                                                                   \n","2000-01-01            0.249303                  0.260273  187.453420   \n","2000-02-01            0.252066                  0.260401  108.823551   \n","2000-03-01            0.247164                  0.265140   58.469634   \n","2000-04-01            0.248209                  0.262308   92.103565   \n","2000-05-01            0.249254                  0.259477  181.052572   \n","...                        ...                       ...         ...   \n","2017-04-01            0.259239                  0.260659  535.702312   \n","2017-05-01            0.265749                  0.254874  370.092425   \n","2017-06-01            0.256290                  0.257576  445.578935   \n","2017-07-01            0.259913                  0.257409  451.197292   \n","2017-08-01            0.264162                  0.254985  402.873332   \n","\n","                EPU_UK  \n","Date                    \n","2000-01-01   53.748451  \n","2000-02-01   58.396511  \n","2000-03-01   61.960393  \n","2000-04-01  114.010429  \n","2000-05-01   46.368325  \n","...                ...  \n","2017-04-01  169.176036  \n","2017-05-01  159.401445  \n","2017-06-01  318.991160  \n","2017-07-01  183.658951  \n","2017-08-01  151.204298  \n","\n","[212 rows x 16 columns]"],"text/html":["\n","  <div id=\"df-a7c9a133-01f9-40d6-9fe5-05430947272e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GBPUSD</th>\n","      <th>CPI_US</th>\n","      <th>CPI_UK</th>\n","      <th>Money Market Rate_US</th>\n","      <th>Money Market Rate_UK</th>\n","      <th>IPI_US</th>\n","      <th>IPI_UK</th>\n","      <th>M2_US</th>\n","      <th>M2_UK</th>\n","      <th>Stock Market News</th>\n","      <th>Economic Development News</th>\n","      <th>FED News</th>\n","      <th>Micro Finance News</th>\n","      <th>International Trade News</th>\n","      <th>EPU_US</th>\n","      <th>EPU_UK</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-01-01</th>\n","      <td>0.609973</td>\n","      <td>164.100</td>\n","      <td>71.936</td>\n","      <td>0.0604</td>\n","      <td>0.0614</td>\n","      <td>94.5458</td>\n","      <td>112.5</td>\n","      <td>4656.2</td>\n","      <td>890517.0</td>\n","      <td>0.258237</td>\n","      <td>0.265871</td>\n","      <td>0.255893</td>\n","      <td>0.249303</td>\n","      <td>0.260273</td>\n","      <td>187.453420</td>\n","      <td>53.748451</td>\n","    </tr>\n","    <tr>\n","      <th>2000-02-01</th>\n","      <td>0.625053</td>\n","      <td>164.400</td>\n","      <td>72.159</td>\n","      <td>0.0610</td>\n","      <td>0.0624</td>\n","      <td>94.8185</td>\n","      <td>113.7</td>\n","      <td>4669.4</td>\n","      <td>873293.0</td>\n","      <td>0.259493</td>\n","      <td>0.261406</td>\n","      <td>0.255016</td>\n","      <td>0.252066</td>\n","      <td>0.260401</td>\n","      <td>108.823551</td>\n","      <td>58.396511</td>\n","    </tr>\n","    <tr>\n","      <th>2000-03-01</th>\n","      <td>0.632823</td>\n","      <td>164.700</td>\n","      <td>72.338</td>\n","      <td>0.0620</td>\n","      <td>0.0623</td>\n","      <td>95.1983</td>\n","      <td>113.6</td>\n","      <td>4699.9</td>\n","      <td>887465.0</td>\n","      <td>0.262109</td>\n","      <td>0.258737</td>\n","      <td>0.254687</td>\n","      <td>0.247164</td>\n","      <td>0.265140</td>\n","      <td>58.469634</td>\n","      <td>61.960393</td>\n","    </tr>\n","    <tr>\n","      <th>2000-04-01</th>\n","      <td>0.631566</td>\n","      <td>164.700</td>\n","      <td>72.573</td>\n","      <td>0.0631</td>\n","      <td>0.0630</td>\n","      <td>95.8921</td>\n","      <td>113.2</td>\n","      <td>4755.9</td>\n","      <td>870743.0</td>\n","      <td>0.259771</td>\n","      <td>0.263898</td>\n","      <td>0.254085</td>\n","      <td>0.248209</td>\n","      <td>0.262308</td>\n","      <td>92.103565</td>\n","      <td>114.010429</td>\n","    </tr>\n","    <tr>\n","      <th>2000-05-01</th>\n","      <td>0.662815</td>\n","      <td>164.800</td>\n","      <td>72.771</td>\n","      <td>0.0676</td>\n","      <td>0.0630</td>\n","      <td>96.0691</td>\n","      <td>113.6</td>\n","      <td>4743.9</td>\n","      <td>845815.0</td>\n","      <td>0.257433</td>\n","      <td>0.269059</td>\n","      <td>0.253482</td>\n","      <td>0.249254</td>\n","      <td>0.259477</td>\n","      <td>181.052572</td>\n","      <td>46.368325</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2017-04-01</th>\n","      <td>0.791045</td>\n","      <td>237.808</td>\n","      <td>102.949</td>\n","      <td>0.0116</td>\n","      <td>0.0033</td>\n","      <td>105.0468</td>\n","      <td>101.8</td>\n","      <td>13452.7</td>\n","      <td>2133785.0</td>\n","      <td>0.255181</td>\n","      <td>0.257543</td>\n","      <td>0.260854</td>\n","      <td>0.259239</td>\n","      <td>0.260659</td>\n","      <td>535.702312</td>\n","      <td>169.176036</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-01</th>\n","      <td>0.773478</td>\n","      <td>238.078</td>\n","      <td>103.309</td>\n","      <td>0.0119</td>\n","      <td>0.0031</td>\n","      <td>105.0136</td>\n","      <td>102.0</td>\n","      <td>13517.8</td>\n","      <td>2128826.0</td>\n","      <td>0.255313</td>\n","      <td>0.251881</td>\n","      <td>0.256059</td>\n","      <td>0.265749</td>\n","      <td>0.254874</td>\n","      <td>370.092425</td>\n","      <td>159.401445</td>\n","    </tr>\n","    <tr>\n","      <th>2017-06-01</th>\n","      <td>0.780442</td>\n","      <td>238.908</td>\n","      <td>103.284</td>\n","      <td>0.0126</td>\n","      <td>0.0029</td>\n","      <td>105.2469</td>\n","      <td>102.4</td>\n","      <td>13544.0</td>\n","      <td>2161166.0</td>\n","      <td>0.255768</td>\n","      <td>0.259531</td>\n","      <td>0.264680</td>\n","      <td>0.256290</td>\n","      <td>0.257576</td>\n","      <td>445.578935</td>\n","      <td>318.991160</td>\n","    </tr>\n","    <tr>\n","      <th>2017-07-01</th>\n","      <td>0.769593</td>\n","      <td>239.362</td>\n","      <td>103.215</td>\n","      <td>0.0131</td>\n","      <td>0.0029</td>\n","      <td>105.1038</td>\n","      <td>102.8</td>\n","      <td>13620.4</td>\n","      <td>2195459.0</td>\n","      <td>0.255551</td>\n","      <td>0.258921</td>\n","      <td>0.262572</td>\n","      <td>0.259913</td>\n","      <td>0.257409</td>\n","      <td>451.197292</td>\n","      <td>183.658951</td>\n","    </tr>\n","    <tr>\n","      <th>2017-08-01</th>\n","      <td>0.771999</td>\n","      <td>239.842</td>\n","      <td>103.785</td>\n","      <td>0.0131</td>\n","      <td>0.0028</td>\n","      <td>104.3403</td>\n","      <td>102.9</td>\n","      <td>13664.4</td>\n","      <td>2150923.0</td>\n","      <td>0.254705</td>\n","      <td>0.256595</td>\n","      <td>0.260185</td>\n","      <td>0.264162</td>\n","      <td>0.254985</td>\n","      <td>402.873332</td>\n","      <td>151.204298</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>212 rows × 16 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7c9a133-01f9-40d6-9fe5-05430947272e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-a7c9a133-01f9-40d6-9fe5-05430947272e button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-a7c9a133-01f9-40d6-9fe5-05430947272e');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-e8707837-0254-44a2-872e-da33e2e81b89\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8707837-0254-44a2-872e-da33e2e81b89')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-e8707837-0254-44a2-872e-da33e2e81b89 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"data","summary":"{\n  \"name\": \"data\",\n  \"rows\": 212,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2000-01-01 00:00:00\",\n        \"max\": \"2017-08-01 00:00:00\",\n        \"num_unique_values\": 212,\n        \"samples\": [\n          \"2002-07-01 00:00:00\",\n          \"2014-06-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GBPUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07277323271848055,\n        \"min\": 0.48307269365725547,\n        \"max\": 0.8110785606831599,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.6432351933551006,\n          0.5915134717193183,\n          0.6336049674629449\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.659319800751,\n        \"min\": 164.1,\n        \"max\": 239.842,\n        \"num_unique_values\": 202,\n        \"samples\": [\n          207.603,\n          170.9,\n          178.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.400193450826142,\n        \"min\": 71.936,\n        \"max\": 103.785,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          74.372,\n          100.203,\n          94.413\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020266283015506273,\n        \"min\": 0.0023,\n        \"max\": 0.0679,\n        \"num_unique_values\": 138,\n        \"samples\": [\n          0.022799999999999997,\n          0.0051,\n          0.057\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02248998379598572,\n        \"min\": 0.0028000000000000004,\n        \"max\": 0.0665,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          0.006500000000000001,\n          0.055700000000000006,\n          0.008100000000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.89908267386627,\n        \"min\": 87.065,\n        \"max\": 106.6134,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          93.5461,\n          105.4698,\n          97.6288\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.695325086932039,\n        \"min\": 95.2,\n        \"max\": 114.3,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          100.4,\n          113.8,\n          114.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2587.5775087515417,\n        \"min\": 4656.2,\n        \"max\": 13664.4,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          5574.9,\n          11370.1,\n          9524.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 710401.8471653981,\n        \"min\": 847.26,\n        \"max\": 2498761.0,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          1059303.0,\n          2498761.0,\n          1953363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Market News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002908404647966463,\n        \"min\": 0.2484262,\n        \"max\": 0.2742658,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.2603932,\n          0.2580105,\n          0.2544306\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economic Development News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0028177243343392687,\n        \"min\": 0.251880854,\n        \"max\": 0.269058652,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.257455101,\n          0.262130912,\n          0.261736333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FED News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0032532151144483872,\n        \"min\": 0.2490762,\n        \"max\": 0.2655337,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.2521258,\n          0.2577743,\n          0.2585963\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro Finance News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0056449680318106,\n        \"min\": 0.244891,\n        \"max\": 0.2725514,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.2555874,\n          0.2530432,\n          0.2512611\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"International Trade News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0032970366533118457,\n        \"min\": 0.2502086,\n        \"max\": 0.2689798,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          0.2643084,\n          0.2559477,\n          0.2559856\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.0760361904917,\n        \"min\": 31.95825829080659,\n        \"max\": 692.2387019062214,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          104.45757657507053,\n          229.16993284336283,\n          185.51310125526527\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 70.29593690168772,\n        \"min\": 24.0359419445406,\n        \"max\": 558.2236420188227,\n        \"num_unique_values\": 212,\n        \"samples\": [\n          88.47257142994314,\n          68.77737113506677,\n          153.32764352624784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":6}],"source":["#Declear that it is a time series data\n","data.index = data.Date\n","data = data.drop('Date', axis=1)\n","data"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":355},"executionInfo":{"elapsed":33,"status":"ok","timestamp":1711918471684,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-120},"id":"GUHXZ2VIDCGb","outputId":"4133d54a-d999-4365-dec4-c56b0d74b2bb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["           GBPUSD      CPI_US      CPI_UK  Money Market Rate_US  \\\n","count  212.000000  212.000000  212.000000            212.000000   \n","mean     0.624364  205.388042   86.634476              0.020400   \n","std      0.072773   23.659320   10.400193              0.020266   \n","min      0.483073  164.100000   71.936000              0.002300   \n","25%      0.570982  183.175000   76.552000              0.003175   \n","50%      0.630161  208.107000   85.643000              0.011650   \n","75%      0.665188  227.963750   98.175000              0.033100   \n","max      0.811079  239.842000  103.785000              0.067900   \n","\n","       Money Market Rate_UK      IPI_US      IPI_UK         M2_US  \\\n","count            212.000000  212.000000  212.000000    212.000000   \n","mean               0.028726   98.724471  105.151415   8404.090566   \n","std                0.022490    4.899083    5.695325   2587.577509   \n","min                0.002800   87.065000   95.200000   4656.200000   \n","25%                0.005875   94.750325   99.700000   6255.900000   \n","50%                0.035050   99.569900  104.750000   7976.800000   \n","75%                0.048900  103.119575  110.400000  10557.225000   \n","max                0.066500  106.613400  114.300000  13664.400000   \n","\n","              M2_UK  Stock Market News  Economic Development News    FED News  \\\n","count  2.120000e+02         212.000000                 212.000000  212.000000   \n","mean   1.578439e+06           0.257548                   0.258559    0.258019   \n","std    7.104018e+05           0.002908                   0.002818    0.003253   \n","min    8.472600e+02           0.248426                   0.251881    0.249076   \n","25%    1.072978e+06           0.255798                   0.256659    0.255679   \n","50%    1.845732e+06           0.257151                   0.258310    0.258170   \n","75%    2.094373e+06           0.258905                   0.260234    0.260513   \n","max    2.498761e+06           0.274266                   0.269059    0.265534   \n","\n","       Micro Finance News  International Trade News      EPU_US      EPU_UK  \n","count          212.000000                212.000000  212.000000  212.000000  \n","mean             0.257254                  0.258634  161.584631  117.236699  \n","std              0.005645                  0.003297  102.076036   70.295937  \n","min              0.244891                  0.250209   31.958258   24.035942  \n","25%              0.252988                  0.256069   91.515842   67.992585  \n","50%              0.256817                  0.257922  137.057354  108.271939  \n","75%              0.260831                  0.260585  198.666496  145.853518  \n","max              0.272551                  0.268980  692.238702  558.223642  "],"text/html":["\n","  <div id=\"df-ce215e83-a3a6-4cae-9070-76329a25fd5b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GBPUSD</th>\n","      <th>CPI_US</th>\n","      <th>CPI_UK</th>\n","      <th>Money Market Rate_US</th>\n","      <th>Money Market Rate_UK</th>\n","      <th>IPI_US</th>\n","      <th>IPI_UK</th>\n","      <th>M2_US</th>\n","      <th>M2_UK</th>\n","      <th>Stock Market News</th>\n","      <th>Economic Development News</th>\n","      <th>FED News</th>\n","      <th>Micro Finance News</th>\n","      <th>International Trade News</th>\n","      <th>EPU_US</th>\n","      <th>EPU_UK</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>2.120000e+02</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","      <td>212.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.624364</td>\n","      <td>205.388042</td>\n","      <td>86.634476</td>\n","      <td>0.020400</td>\n","      <td>0.028726</td>\n","      <td>98.724471</td>\n","      <td>105.151415</td>\n","      <td>8404.090566</td>\n","      <td>1.578439e+06</td>\n","      <td>0.257548</td>\n","      <td>0.258559</td>\n","      <td>0.258019</td>\n","      <td>0.257254</td>\n","      <td>0.258634</td>\n","      <td>161.584631</td>\n","      <td>117.236699</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.072773</td>\n","      <td>23.659320</td>\n","      <td>10.400193</td>\n","      <td>0.020266</td>\n","      <td>0.022490</td>\n","      <td>4.899083</td>\n","      <td>5.695325</td>\n","      <td>2587.577509</td>\n","      <td>7.104018e+05</td>\n","      <td>0.002908</td>\n","      <td>0.002818</td>\n","      <td>0.003253</td>\n","      <td>0.005645</td>\n","      <td>0.003297</td>\n","      <td>102.076036</td>\n","      <td>70.295937</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.483073</td>\n","      <td>164.100000</td>\n","      <td>71.936000</td>\n","      <td>0.002300</td>\n","      <td>0.002800</td>\n","      <td>87.065000</td>\n","      <td>95.200000</td>\n","      <td>4656.200000</td>\n","      <td>8.472600e+02</td>\n","      <td>0.248426</td>\n","      <td>0.251881</td>\n","      <td>0.249076</td>\n","      <td>0.244891</td>\n","      <td>0.250209</td>\n","      <td>31.958258</td>\n","      <td>24.035942</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.570982</td>\n","      <td>183.175000</td>\n","      <td>76.552000</td>\n","      <td>0.003175</td>\n","      <td>0.005875</td>\n","      <td>94.750325</td>\n","      <td>99.700000</td>\n","      <td>6255.900000</td>\n","      <td>1.072978e+06</td>\n","      <td>0.255798</td>\n","      <td>0.256659</td>\n","      <td>0.255679</td>\n","      <td>0.252988</td>\n","      <td>0.256069</td>\n","      <td>91.515842</td>\n","      <td>67.992585</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.630161</td>\n","      <td>208.107000</td>\n","      <td>85.643000</td>\n","      <td>0.011650</td>\n","      <td>0.035050</td>\n","      <td>99.569900</td>\n","      <td>104.750000</td>\n","      <td>7976.800000</td>\n","      <td>1.845732e+06</td>\n","      <td>0.257151</td>\n","      <td>0.258310</td>\n","      <td>0.258170</td>\n","      <td>0.256817</td>\n","      <td>0.257922</td>\n","      <td>137.057354</td>\n","      <td>108.271939</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.665188</td>\n","      <td>227.963750</td>\n","      <td>98.175000</td>\n","      <td>0.033100</td>\n","      <td>0.048900</td>\n","      <td>103.119575</td>\n","      <td>110.400000</td>\n","      <td>10557.225000</td>\n","      <td>2.094373e+06</td>\n","      <td>0.258905</td>\n","      <td>0.260234</td>\n","      <td>0.260513</td>\n","      <td>0.260831</td>\n","      <td>0.260585</td>\n","      <td>198.666496</td>\n","      <td>145.853518</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.811079</td>\n","      <td>239.842000</td>\n","      <td>103.785000</td>\n","      <td>0.067900</td>\n","      <td>0.066500</td>\n","      <td>106.613400</td>\n","      <td>114.300000</td>\n","      <td>13664.400000</td>\n","      <td>2.498761e+06</td>\n","      <td>0.274266</td>\n","      <td>0.269059</td>\n","      <td>0.265534</td>\n","      <td>0.272551</td>\n","      <td>0.268980</td>\n","      <td>692.238702</td>\n","      <td>558.223642</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce215e83-a3a6-4cae-9070-76329a25fd5b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-ce215e83-a3a6-4cae-9070-76329a25fd5b button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-ce215e83-a3a6-4cae-9070-76329a25fd5b');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-0d3b8d54-244b-41b1-b482-d2842eca3921\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d3b8d54-244b-41b1-b482-d2842eca3921')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-0d3b8d54-244b-41b1-b482-d2842eca3921 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"GBPUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.75879108129271,\n        \"min\": 0.07277323271848055,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6243643625792313,\n          0.6301613403372024,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68.63154600949093,\n        \"min\": 23.659319800751,\n        \"max\": 239.842,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          205.38804245283018,\n          208.107,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.97900198147013,\n        \"min\": 10.400193450826142,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          86.63447641509433,\n          85.643,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.94530154830265,\n        \"min\": 0.0023,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.02040047169811321,\n          0.01165,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.94269793433888,\n        \"min\": 0.0028000000000000004,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.028725943396226417,\n          0.03505,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.84756952196388,\n        \"min\": 4.89908267386627,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          98.7244712264151,\n          99.5699,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.47690607356531,\n        \"min\": 5.695325086932039,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          105.15141509433961,\n          104.75,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4329.829128695503,\n        \"min\": 212.0,\n        \"max\": 13664.4,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8404.090566037734,\n          7976.8,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 939230.6917641549,\n        \"min\": 212.0,\n        \"max\": 2498761.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1578438.5039150945,\n          1845732.0,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Market News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.87483318659591,\n        \"min\": 0.002908404647966463,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.25754829386792455,\n          0.2571506,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economic Development News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.87470610970051,\n        \"min\": 0.0028177243343392687,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.25855851678773584,\n          0.2583104335,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FED News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.87507280437444,\n        \"min\": 0.0032532151144483872,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.25801910849056603,\n          0.25816995,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro Finance News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.87503489464112,\n        \"min\": 0.0056449680318106,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2572544688679245,\n          0.25681735,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"International Trade News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.87479782916365,\n        \"min\": 0.0032970366533118457,\n        \"max\": 212.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2586339254716981,\n          0.25792205,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 206.11591549886387,\n        \"min\": 31.95825829080659,\n        \"max\": 692.2387019062214,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          161.58463103286505,\n          137.0573536173838,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 169.4181766677176,\n        \"min\": 24.0359419445406,\n        \"max\": 558.2236420188227,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          117.23669926252549,\n          108.27193948543771,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":7}],"source":[" data.describe()"]},{"cell_type":"markdown","source":["# Johansen Test on row data"],"metadata":{"id":"HdHcgRpmW7eM"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","from statsmodels.tsa.stattools import coint\n","# Define the pairs of variables for cointegration testing\n","pairs = [\n","    ('GBPUSD', 'Stock Market News'),\n","    ('GBPUSD', 'Economic Development News'),\n","    ('GBPUSD', 'FED News'),\n","    ('GBPUSD', 'M2_US'),\n","    ('GBPUSD', 'EPU_US'),\n","    ('GBPUSD', 'EPU_UK')\n","]\n","\n","# Store the p-values for each pair of variables\n","p_values = {}\n","\n","# Perform cointegration tests for each pair\n","for pair in pairs:\n","    result_coint = coint(data[pair[0]], data[pair[1]])\n","    p_values[pair] = result_coint[1]\n","\n","# Print the p-values for each pair of variables\n","for pair, p_value in p_values.items():\n","    print(f\"Cointegration test for {pair}: p-value = {p_value}\")\n","#p_values>0.05, weak evidence against the null hypothesis of no cointegration. i.e. these news and cointegrate!"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zesEdnK3WyUd","executionInfo":{"status":"ok","timestamp":1711918471685,"user_tz":-120,"elapsed":32,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"ba4ee229-90bc-41ed-bbe2-6b6e0a528ddd"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Cointegration test for ('GBPUSD', 'Stock Market News'): p-value = 0.6817805198046707\n","Cointegration test for ('GBPUSD', 'Economic Development News'): p-value = 0.6807228077547767\n","Cointegration test for ('GBPUSD', 'FED News'): p-value = 0.897439985757061\n","Cointegration test for ('GBPUSD', 'M2_US'): p-value = 0.40081363718578344\n","Cointegration test for ('GBPUSD', 'EPU_US'): p-value = 0.41464509755284534\n","Cointegration test for ('GBPUSD', 'EPU_UK'): p-value = 0.5373983931528907\n"]}]},{"cell_type":"code","source":["######Please referr to r code for the full version of johansen test with 16 variables. Here we check only 12 variables as critical values are calculated only for maximum 12 variables, for demonstrating."],"metadata":{"id":"vtEgrNPTpWQ7","executionInfo":{"status":"ok","timestamp":1711918471685,"user_tz":-120,"elapsed":27,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["#here we check only 12 variables, referr to r for the full version with 16 variables\n","\n","from statsmodels.tsa.vector_ar.vecm import coint_johansen\n","\n","# Assuming 'data' is your DataFrame containing the variables\n","\n","# Perform the Johansen cointegration test\n","result = coint_johansen(data.iloc[:,:12], det_order=0, k_ar_diff=1)\n","\n","# Extract the test statistics and critical values\n","test_statistics = result.lr1\n","critical_values = result.cvt\n","\n","# Print the test statistics and critical values\n","print(\"Test statistics:\", test_statistics)\n","print(\"Critical values:\\n\", critical_values)\n","# Compare test statistics to critical values\n","test_vs_critical = test_statistics[:, np.newaxis] > critical_values[:, 1]\n","# Calculate the p-values\n","p_values = (test_vs_critical).mean(axis=1)\n","\n","# Print the p-values\n","print(\"P-values:\", p_values)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"U6kkRNwDYjHZ","executionInfo":{"status":"ok","timestamp":1711918471685,"user_tz":-120,"elapsed":26,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"6254e8a1-c4e5-4b90-889e-33dc78c86404"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Test statistics: [6.20570146e+02 4.84996693e+02 3.79118742e+02 2.94060280e+02\n"," 2.13874413e+02 1.51548133e+02 9.94944076e+01 6.66416915e+01\n"," 3.53273561e+01 1.55791852e+01 6.37303702e+00 8.75906660e-02]\n","Critical values:\n"," [[326.5354 334.9795 351.215 ]\n"," [277.374  285.1402 300.2821]\n"," [232.103  239.2468 253.2526]\n"," [190.8714 197.3772 210.0366]\n"," [153.6341 159.529  171.0905]\n"," [120.3673 125.6185 135.9825]\n"," [ 91.109   95.7542 104.9637]\n"," [ 65.8202  69.8189  77.8202]\n"," [ 44.4929  47.8545  54.6815]\n"," [ 27.0669  29.7961  35.4628]\n"," [ 13.4294  15.4943  19.9349]\n"," [  2.7055   3.8415   6.6349]]\n","P-values: [1.         1.         1.         0.91666667 0.75       0.58333333\n"," 0.5        0.33333333 0.25       0.16666667 0.08333333 0.        ]\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import statsmodels.api as sm\n","from statsmodels.tsa.vector_ar.vecm import coint_johansen\n","\n","# Assuming 'data' is your DataFrame containing the time series data\n","\n","# Step 1: Perform Johansen cointegration test\n","def johansen_cointegration_test(data):\n","    result = coint_johansen(data, det_order=0, k_ar_diff=1)\n","    print(\"Eigenvalues:\", result.eig)\n","    print(\"Trace Statistic:\", result.lr1)\n","    print(\"Critical Values (90%, 95%, 99%):\", result.cvt)\n","\n","    # Get the number of cointegrating vectors (rank) using maximum eigenvalue test\n","    rank_max_eig = result.ind\n","    print(\"Rank (using max eigenvalue test):\", rank_max_eig)\n","\n","    # Get the number of cointegrating vectors (rank) using trace test\n","    rank_trace = result.lr2\n","    print(\"Rank (using trace test):\", rank_trace)\n","\n","    return result\n","\n","johansen_result = johansen_cointegration_test(data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zL4ZGaQnW6On","executionInfo":{"status":"ok","timestamp":1711918471686,"user_tz":-120,"elapsed":23,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"c5569ea7-4bf5-4ece-aa63-ab62d7f10428"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Eigenvalues: [5.36342221e-01 5.07359094e-01 4.51570775e-01 4.18277382e-01\n"," 3.34050305e-01 3.18363579e-01 2.71013233e-01 2.53385219e-01\n"," 2.23167285e-01 1.99544397e-01 1.37284082e-01 1.25422258e-01\n"," 8.85761068e-02 4.04364946e-02 2.67784962e-02 1.27144547e-04]\n","Trace Statistic: [1.03639842e+03 8.74990627e+02 7.26315928e+02 6.00169549e+02\n"," 4.86399625e+02 4.01025984e+02 3.20541621e+02 2.54160684e+02\n"," 1.92797442e+02 1.39766090e+02 9.30255062e+01 6.20148437e+01\n"," 3.38718848e+01 1.43949760e+01 5.72685225e+00 2.67020523e-02]\n","Critical Values (90%, 95%, 99%): [[     nan      nan      nan]\n"," [     nan      nan      nan]\n"," [     nan      nan      nan]\n"," [     nan      nan      nan]\n"," [326.5354 334.9795 351.215 ]\n"," [277.374  285.1402 300.2821]\n"," [232.103  239.2468 253.2526]\n"," [190.8714 197.3772 210.0366]\n"," [153.6341 159.529  171.0905]\n"," [120.3673 125.6185 135.9825]\n"," [ 91.109   95.7542 104.9637]\n"," [ 65.8202  69.8189  77.8202]\n"," [ 44.4929  47.8545  54.6815]\n"," [ 27.0669  29.7961  35.4628]\n"," [ 13.4294  15.4943  19.9349]\n"," [  2.7055   3.8415   6.6349]]\n","Rank (using max eigenvalue test): [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]\n","Rank (using trace test): [1.61407794e+02 1.48674699e+02 1.26146379e+02 1.13769925e+02\n"," 8.53736404e+01 8.04843627e+01 6.63809369e+01 6.13632421e+01\n"," 5.30313520e+01 4.67405841e+01 3.10106626e+01 2.81429589e+01\n"," 1.94769088e+01 8.66812375e+00 5.70015020e+00 2.67020523e-02]\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-11-7449a5313a5b>:10: HypothesisTestWarning: Critical values are only available for time series with 12 variables at most.\n","  result = coint_johansen(data, det_order=0, k_ar_diff=1)\n"]}]},{"cell_type":"code","source":["#This suggests that there is insufficient evidence to conclude that the residuals of johansen cointegration test are stationary. i.e. we do should go for VAR model!"],"metadata":{"id":"ktPvSJeRsPm2","executionInfo":{"status":"ok","timestamp":1711918471687,"user_tz":-120,"elapsed":20,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["#This suggests that there is insufficient evidence to conclude that the residuals of johansen cointegration test are stationary. i.e. we do should go for VAR model!\n","\n","from statsmodels.tsa.stattools import adfuller\n","\n","jotest = coint_johansen(data, det_order=0, k_ar_diff=6) #4 2\n","# Print the summary\n","#print(\"Eigenvalues:\")\n","#print(jotest.eig)\n","#print(\"\\nEigenvectors:\")\n","#print(jotest.evec)\n","#print(\"\\nCritical values (90%, 95%, 99%):\")\n","#print(jotest.cvt)\n","#print(\"\\nTrace statistic and critical values:\")\n","#print(\"Trace statistic:\", jotest.lr1)\n","#print(\"Critical values (90%, 95%, 99%):\", jotest.cvm)\n","#print(\"\\nMaximum eigenvalue statistic and critical values:\")\n","#print(\"Maximum eigenvalue statistic:\", jotest.lr2)\n","#print(\"Critical values (90%, 95%, 99%):\", jotest.cvm)\n","#first_eigenvector = [2.20705422e+01, 1.96067863e+00, -2.03628052e+01, -2.48325292e+01, -2.60014714e+01, -1.05021603e+00, -1.66888390e+01, 9.73382101e+00, 1.45157187e+00, -2.85781928e+01, 1.18417956e+01, -1.18091693e+01, -1.12779071e+00, 1.21216745e+00, -2.03628309e+00, 5.88940695e+00]\n","#as there is a bag in r adfuller test, we conduct the adfuller test here.\n","\n","w = (1.000000 * data.iloc[:, 0] + 802.1769 * data.iloc[:, 1] - 572.4314 * data.iloc[:, 2] - 0.03954655 * data.iloc[:, 3] + 37.72297 * data.iloc[:, 4] + 144.4919 * data.iloc[:, 5] - 142.5813 * data.iloc[:, 6] + 0.01644418 * data.iloc[:, 7] + 0.00001548652 * data.iloc[:, 8] - 8312.689 * data.iloc[:, 9] - 1706.767 * data.iloc[:, 10] + 1003.942 * data.iloc[:, 11] - 919.0407 * data.iloc[:, 12] - 234.6682 * data.iloc[:, 13] - 0.01478644 * data.iloc[:, 14] - 0.003823252 * data.iloc[:, 15]) #for 6 lags\n","#w = (1.000000 * data.iloc[:, 0] - 4.507364 * data.iloc[:, 1] + 1.239260 * data.iloc[:, 2] - 0.078895 * data.iloc[:, 3] - 0.031615 * data.iloc[:, 4] - 0.267858 * data.iloc[:, 5] + 0.166605 * data.iloc[:, 6] - 0.000028 * data.iloc[:, 7] + 0.000004 * data.iloc[:, 8] - 5.108912 * data.iloc[:, 9] + 0.280642 * data.iloc[:, 10] + 36.538940 * data.iloc[:, 11] - 30.943740 * data.iloc[:, 12] + 3.264700 * data.iloc[:, 13]) #for 4 lags\n","\n","# Convert the calculated series to a pandas Series\n","w_ts = pd.Series(w)\n","\n","# Assuming Monthly_UK_to_USD_ts is your time series data for comparison\n","# Perform ADF test\n","adf_result = adfuller(data.iloc[:, 0].values - w_ts.values)\n","print(\"ADF Statistic:\", adf_result[0])\n","print(\"p-value:\", adf_result[1])\n","print(\"Critical Values:\", adf_result[4])\n","#This p-value >0.05, and suggests that there is insufficient evidence to conclude that the data is stationary."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2GY4zkX7hP3_","executionInfo":{"status":"ok","timestamp":1711918471687,"user_tz":-120,"elapsed":19,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"fb2d895d-daa9-4a68-c045-5aa76204e7f5"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["ADF Statistic: -1.5312878419182323\n","p-value: 0.5179808483365343\n","Critical Values: {'1%': -3.461878735881654, '5%': -2.875403665910809, '10%': -2.574159410430839}\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-13-cca07582c8aa>:5: HypothesisTestWarning: Critical values are only available for time series with 12 variables at most.\n","  jotest = coint_johansen(data, det_order=0, k_ar_diff=6) #4 2\n"]}]},{"cell_type":"markdown","source":["# Data Cleaning"],"metadata":{"id":"Q9LFLvXd4Vy1"}},{"cell_type":"code","execution_count":14,"metadata":{"id":"cSnsSZCBiB3I","executionInfo":{"status":"ok","timestamp":1711918471687,"user_tz":-120,"elapsed":15,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}}},"outputs":[],"source":["# calculate returns of exchange rate (log FX-log of lagFX)\n","#data['USDGBP'] = np.log(data['USDGBP'])-np.log(data['USDGBP'].shift(1))\n","#data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5xhSYPxEzPjk"},"outputs":[],"source":["# Take LOGS\n","data['GBPUSD'] = np.log(data['GBPUSD'])\n","data['CPI_US'] = np.log(data['CPI_US'])\n","data['CPI_UK'] = np.log(data['CPI_UK'])\n","#data['Money Market Rate_US'] = np.log(data['Money Market Rate_US']) # There's typically no compelling reason to logarithmize money market rates.\n","#data['Money Market Rate_UK'] = np.log(data['Money Market Rate_UK']) # It is common practice not to logarithmize Money market rates. They don't exhibit multiplicative growth patterns in their natural form. My supervisor also suggested me not to logarithmize the interest rate.\n","data['IPI_US'] = np.log(data['IPI_US'])\n","data['IPI_UK'] = np.log(data['IPI_UK'])\n","data['M2_US'] = np.log(data['M2_US'])\n","data['M2_UK'] = np.log(data['M2_UK'])\n","data['Stock Market News'] = np.log(data['Stock Market News'])\n","data['Economic Development News'] = np.log(data['Economic Development News'])\n","data['FED News'] = np.log(data['FED News'])\n","data['Micro Finance News'] = np.log(data['Micro Finance News']) #we call this \"microeconomic news\"\n","data['International Trade News'] = np.log(data['International Trade News'])\n","data['EPU_US'] = np.log(data['EPU_US'])\n","data['EPU_UK'] = np.log(data['EPU_UK'])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1711360723049,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"dPC0tS23qXn2","outputId":"a7ce2177-8d5c-4e79-e18e-1a2fc7baa201"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  \\\n","Date                                                             \n","2000-01-01       NaN       NaN       NaN                   NaN   \n","2000-02-01  0.024422  0.001826  0.003095                0.0006   \n","2000-03-01  0.012355  0.001823  0.002478                0.0010   \n","2000-04-01 -0.001989  0.000000  0.003243                0.0011   \n","2000-05-01  0.048292  0.000607  0.002725                0.0045   \n","...              ...       ...       ...                   ...   \n","2017-04-01 -0.023466 -0.001252  0.004547                0.0003   \n","2017-05-01 -0.022458  0.001135  0.003491                0.0003   \n","2017-06-01  0.008963  0.003480 -0.000242                0.0007   \n","2017-07-01 -0.013999  0.001899 -0.000668                0.0005   \n","2017-08-01  0.003121  0.002003  0.005507                0.0000   \n","\n","            Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  \\\n","Date                                                                       \n","2000-01-01                   NaN       NaN       NaN       NaN       NaN   \n","2000-02-01                0.0010  0.002880  0.010610  0.002831 -0.019531   \n","2000-03-01               -0.0001  0.003998 -0.000880  0.006511  0.016098   \n","2000-04-01                0.0007  0.007262 -0.003527  0.011845 -0.019022   \n","2000-05-01                0.0000  0.001844  0.003527 -0.002526 -0.029046   \n","...                          ...       ...       ...       ...       ...   \n","2017-04-01               -0.0002  0.010821  0.001967  0.003895  0.034527   \n","2017-05-01               -0.0002 -0.000316  0.001963  0.004828 -0.002327   \n","2017-06-01               -0.0002  0.002219  0.003914  0.001936  0.015077   \n","2017-07-01                0.0000 -0.001361  0.003899  0.005625  0.015743   \n","2017-08-01               -0.0001 -0.007291  0.000972  0.003225 -0.020494   \n","\n","            Stock Market News  Economic Development News  FED News  \\\n","Date                                                                 \n","2000-01-01                NaN                        NaN       NaN   \n","2000-02-01           0.004850                  -0.016937 -0.003431   \n","2000-03-01           0.010032                  -0.010264 -0.001292   \n","2000-04-01          -0.008960                   0.019750 -0.002368   \n","2000-05-01          -0.009041                   0.019368 -0.002373   \n","...                       ...                        ...       ...   \n","2017-04-01           0.002005                  -0.006553 -0.003000   \n","2017-05-01           0.000518                  -0.022231 -0.018553   \n","2017-06-01           0.001780                   0.029919  0.033112   \n","2017-07-01          -0.000847                  -0.002352 -0.007995   \n","2017-08-01          -0.003318                  -0.009024 -0.009132   \n","\n","            Micro Finance News  International Trade News    EPU_US    EPU_UK  \n","Date                                                                          \n","2000-01-01                 NaN                       NaN       NaN       NaN  \n","2000-02-01            0.011022                  0.000489 -0.543803  0.082941  \n","2000-03-01           -0.019641                  0.018035 -0.621220  0.059239  \n","2000-04-01            0.004221                 -0.010737  0.454406  0.609795  \n","2000-05-01            0.004203                 -0.010853  0.675874 -0.899673  \n","...                        ...                       ...       ...       ...  \n","2017-04-01            0.001977                  0.011571  0.750910 -0.317777  \n","2017-05-01            0.024800                 -0.022444 -0.369826 -0.059514  \n","2017-06-01           -0.036240                  0.010546  0.185622  0.693738  \n","2017-07-01            0.014035                 -0.000650  0.012530 -0.552083  \n","2017-08-01            0.016215                 -0.009463 -0.113282 -0.194449  \n","\n","[212 rows x 16 columns]"],"text/html":["\n","  <div id=\"df-b6ab48fe-a93c-44dc-9e54-8f01750e928c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GBPUSD</th>\n","      <th>CPI_US</th>\n","      <th>CPI_UK</th>\n","      <th>Money Market Rate_US</th>\n","      <th>Money Market Rate_UK</th>\n","      <th>IPI_US</th>\n","      <th>IPI_UK</th>\n","      <th>M2_US</th>\n","      <th>M2_UK</th>\n","      <th>Stock Market News</th>\n","      <th>Economic Development News</th>\n","      <th>FED News</th>\n","      <th>Micro Finance News</th>\n","      <th>International Trade News</th>\n","      <th>EPU_US</th>\n","      <th>EPU_UK</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-01-01</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2000-02-01</th>\n","      <td>0.024422</td>\n","      <td>0.001826</td>\n","      <td>0.003095</td>\n","      <td>0.0006</td>\n","      <td>0.0010</td>\n","      <td>0.002880</td>\n","      <td>0.010610</td>\n","      <td>0.002831</td>\n","      <td>-0.019531</td>\n","      <td>0.004850</td>\n","      <td>-0.016937</td>\n","      <td>-0.003431</td>\n","      <td>0.011022</td>\n","      <td>0.000489</td>\n","      <td>-0.543803</td>\n","      <td>0.082941</td>\n","    </tr>\n","    <tr>\n","      <th>2000-03-01</th>\n","      <td>0.012355</td>\n","      <td>0.001823</td>\n","      <td>0.002478</td>\n","      <td>0.0010</td>\n","      <td>-0.0001</td>\n","      <td>0.003998</td>\n","      <td>-0.000880</td>\n","      <td>0.006511</td>\n","      <td>0.016098</td>\n","      <td>0.010032</td>\n","      <td>-0.010264</td>\n","      <td>-0.001292</td>\n","      <td>-0.019641</td>\n","      <td>0.018035</td>\n","      <td>-0.621220</td>\n","      <td>0.059239</td>\n","    </tr>\n","    <tr>\n","      <th>2000-04-01</th>\n","      <td>-0.001989</td>\n","      <td>0.000000</td>\n","      <td>0.003243</td>\n","      <td>0.0011</td>\n","      <td>0.0007</td>\n","      <td>0.007262</td>\n","      <td>-0.003527</td>\n","      <td>0.011845</td>\n","      <td>-0.019022</td>\n","      <td>-0.008960</td>\n","      <td>0.019750</td>\n","      <td>-0.002368</td>\n","      <td>0.004221</td>\n","      <td>-0.010737</td>\n","      <td>0.454406</td>\n","      <td>0.609795</td>\n","    </tr>\n","    <tr>\n","      <th>2000-05-01</th>\n","      <td>0.048292</td>\n","      <td>0.000607</td>\n","      <td>0.002725</td>\n","      <td>0.0045</td>\n","      <td>0.0000</td>\n","      <td>0.001844</td>\n","      <td>0.003527</td>\n","      <td>-0.002526</td>\n","      <td>-0.029046</td>\n","      <td>-0.009041</td>\n","      <td>0.019368</td>\n","      <td>-0.002373</td>\n","      <td>0.004203</td>\n","      <td>-0.010853</td>\n","      <td>0.675874</td>\n","      <td>-0.899673</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2017-04-01</th>\n","      <td>-0.023466</td>\n","      <td>-0.001252</td>\n","      <td>0.004547</td>\n","      <td>0.0003</td>\n","      <td>-0.0002</td>\n","      <td>0.010821</td>\n","      <td>0.001967</td>\n","      <td>0.003895</td>\n","      <td>0.034527</td>\n","      <td>0.002005</td>\n","      <td>-0.006553</td>\n","      <td>-0.003000</td>\n","      <td>0.001977</td>\n","      <td>0.011571</td>\n","      <td>0.750910</td>\n","      <td>-0.317777</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-01</th>\n","      <td>-0.022458</td>\n","      <td>0.001135</td>\n","      <td>0.003491</td>\n","      <td>0.0003</td>\n","      <td>-0.0002</td>\n","      <td>-0.000316</td>\n","      <td>0.001963</td>\n","      <td>0.004828</td>\n","      <td>-0.002327</td>\n","      <td>0.000518</td>\n","      <td>-0.022231</td>\n","      <td>-0.018553</td>\n","      <td>0.024800</td>\n","      <td>-0.022444</td>\n","      <td>-0.369826</td>\n","      <td>-0.059514</td>\n","    </tr>\n","    <tr>\n","      <th>2017-06-01</th>\n","      <td>0.008963</td>\n","      <td>0.003480</td>\n","      <td>-0.000242</td>\n","      <td>0.0007</td>\n","      <td>-0.0002</td>\n","      <td>0.002219</td>\n","      <td>0.003914</td>\n","      <td>0.001936</td>\n","      <td>0.015077</td>\n","      <td>0.001780</td>\n","      <td>0.029919</td>\n","      <td>0.033112</td>\n","      <td>-0.036240</td>\n","      <td>0.010546</td>\n","      <td>0.185622</td>\n","      <td>0.693738</td>\n","    </tr>\n","    <tr>\n","      <th>2017-07-01</th>\n","      <td>-0.013999</td>\n","      <td>0.001899</td>\n","      <td>-0.000668</td>\n","      <td>0.0005</td>\n","      <td>0.0000</td>\n","      <td>-0.001361</td>\n","      <td>0.003899</td>\n","      <td>0.005625</td>\n","      <td>0.015743</td>\n","      <td>-0.000847</td>\n","      <td>-0.002352</td>\n","      <td>-0.007995</td>\n","      <td>0.014035</td>\n","      <td>-0.000650</td>\n","      <td>0.012530</td>\n","      <td>-0.552083</td>\n","    </tr>\n","    <tr>\n","      <th>2017-08-01</th>\n","      <td>0.003121</td>\n","      <td>0.002003</td>\n","      <td>0.005507</td>\n","      <td>0.0000</td>\n","      <td>-0.0001</td>\n","      <td>-0.007291</td>\n","      <td>0.000972</td>\n","      <td>0.003225</td>\n","      <td>-0.020494</td>\n","      <td>-0.003318</td>\n","      <td>-0.009024</td>\n","      <td>-0.009132</td>\n","      <td>0.016215</td>\n","      <td>-0.009463</td>\n","      <td>-0.113282</td>\n","      <td>-0.194449</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>212 rows × 16 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6ab48fe-a93c-44dc-9e54-8f01750e928c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-b6ab48fe-a93c-44dc-9e54-8f01750e928c button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-b6ab48fe-a93c-44dc-9e54-8f01750e928c');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-79ea2c01-bb6f-4a62-b68a-3df767ccac3c\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79ea2c01-bb6f-4a62-b68a-3df767ccac3c')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-79ea2c01-bb6f-4a62-b68a-3df767ccac3c button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"data","summary":"{\n  \"name\": \"data\",\n  \"rows\": 212,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2000-01-01 00:00:00\",\n        \"max\": \"2017-08-01 00:00:00\",\n        \"num_unique_values\": 212,\n        \"samples\": [\n          \"2002-07-01 00:00:00\",\n          \"2014-06-01 00:00:00\",\n          \"2011-09-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GBPUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021903039674555987,\n        \"min\": -0.058966413940568896,\n        \"max\": 0.09703159622393576,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.011393159398264796,\n          -0.009572377962586143,\n          0.0008932057891550826\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0030452458746710155,\n        \"min\": -0.017864094092818306,\n        \"max\": 0.013674561020173392,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          -0.0005849663811146044,\n          0.0017862463814637408,\n          0.004228600245438585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0035140390262740503,\n        \"min\": -0.0088780916994331,\n        \"max\": 0.009945929974087164,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.00287329511981671,\n          -0.003408895411168622,\n          0.0006988120480055926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0022478208811221295,\n        \"min\": -0.0178,\n        \"max\": 0.009399999999999995,\n        \"num_unique_values\": 116,\n        \"samples\": [\n          -0.0011999999999999997,\n          0.0003000000000000086,\n          -9.999999999999766e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0019111518691452773,\n        \"min\": -0.016799999999999995,\n        \"max\": 0.0040000000000000105,\n        \"num_unique_values\": 106,\n        \"samples\": [\n          -0.000599999999999999,\n          -0.0011000000000000038,\n          -0.0006999999999999923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006684355386162238,\n        \"min\": -0.044008207471526006,\n        \"max\": 0.014914905081914398,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.00032492066984435297,\n          0.00020951670450930493,\n          0.007079343191222165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00952707668395981,\n        \"min\": -0.04825697300777332,\n        \"max\": 0.025369194125459238,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          -0.008260717007366303,\n          0.0008948546458436013,\n          0.0090744724334062\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003761690427698376,\n        \"min\": -0.004561518312819146,\n        \"max\": 0.022217582227465726,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.0075589391463104505,\n          0.0052106295194391805,\n          0.003458687890089962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1637295133229077,\n        \"min\": -6.92642455773913,\n        \"max\": 7.001626503142007,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          -0.0031381110210766394,\n          -0.009285154360977543,\n          0.03318702403330853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Market News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01111449287174326,\n        \"min\": -0.04840344889516035,\n        \"max\": 0.062334576139732034,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          -0.006122164257193141,\n          0.02842697069904765,\n          -0.00403674016104949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economic Development News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011655893940569147,\n        \"min\": -0.03557577033031167,\n        \"max\": 0.029919311413495908,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.003063069986759537,\n          -0.008853670171760086,\n          -0.021085634692148192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FED News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011298308073668356,\n        \"min\": -0.02470761491829343,\n        \"max\": 0.0331117479536549,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.005575288953607815,\n          -0.017589757881970813,\n          -0.007258308296627991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro Finance News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02590033189827522,\n        \"min\": -0.08210857399910654,\n        \"max\": 0.056793254581466224,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.013929051813427407,\n          0.006864536966335155,\n          0.017304166832532664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"International Trade News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010995532421307946,\n        \"min\": -0.027712976353242214,\n        \"max\": 0.03900440702551222,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          -0.015780955301934796,\n          -0.008632660457603691,\n          0.007749628394848829\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5645373231907893,\n        \"min\": -1.8334616248313114,\n        \"max\": 1.502079241540831,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.21884889435386246,\n          0.7838847473287993,\n          -0.4650954530240643\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3110397840068799,\n        \"min\": -0.8996733444161249,\n        \"max\": 1.0553582936828247,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.05766166184304833,\n          -0.09834455523756969,\n          -0.024004546230261603\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":16}],"source":["# First Differencing\n","#data['USDGBP'] = np.log(data['USDGBP'])\n","data = data.diff()\n","data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"elapsed":411,"status":"ok","timestamp":1711360723448,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"ESPEu46m-Ewy","outputId":"32168bb1-8c81-4d7c-faa5-879198de87d0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  \\\n","Date                                                             \n","2000-02-01  0.024422  0.001826  0.003095                0.0006   \n","2000-03-01  0.012355  0.001823  0.002478                0.0010   \n","2000-04-01 -0.001989  0.000000  0.003243                0.0011   \n","2000-05-01  0.048292  0.000607  0.002725                0.0045   \n","2000-06-01 -0.000115  0.006653  0.001442                0.0003   \n","...              ...       ...       ...                   ...   \n","2017-04-01 -0.023466 -0.001252  0.004547                0.0003   \n","2017-05-01 -0.022458  0.001135  0.003491                0.0003   \n","2017-06-01  0.008963  0.003480 -0.000242                0.0007   \n","2017-07-01 -0.013999  0.001899 -0.000668                0.0005   \n","2017-08-01  0.003121  0.002003  0.005507                0.0000   \n","\n","            Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  \\\n","Date                                                                       \n","2000-02-01                0.0010  0.002880  0.010610  0.002831 -0.019531   \n","2000-03-01               -0.0001  0.003998 -0.000880  0.006511  0.016098   \n","2000-04-01                0.0007  0.007262 -0.003527  0.011845 -0.019022   \n","2000-05-01                0.0000  0.001844  0.003527 -0.002526 -0.029046   \n","2000-06-01               -0.0007  0.000912  0.005268  0.003766  0.015023   \n","...                          ...       ...       ...       ...       ...   \n","2017-04-01               -0.0002  0.010821  0.001967  0.003895  0.034527   \n","2017-05-01               -0.0002 -0.000316  0.001963  0.004828 -0.002327   \n","2017-06-01               -0.0002  0.002219  0.003914  0.001936  0.015077   \n","2017-07-01                0.0000 -0.001361  0.003899  0.005625  0.015743   \n","2017-08-01               -0.0001 -0.007291  0.000972  0.003225 -0.020494   \n","\n","            Stock Market News  Economic Development News  FED News  \\\n","Date                                                                 \n","2000-02-01           0.004850                  -0.016937 -0.003431   \n","2000-03-01           0.010032                  -0.010264 -0.001292   \n","2000-04-01          -0.008960                   0.019750 -0.002368   \n","2000-05-01          -0.009041                   0.019368 -0.002373   \n","2000-06-01          -0.002734                  -0.035576  0.013077   \n","...                       ...                        ...       ...   \n","2017-04-01           0.002005                  -0.006553 -0.003000   \n","2017-05-01           0.000518                  -0.022231 -0.018553   \n","2017-06-01           0.001780                   0.029919  0.033112   \n","2017-07-01          -0.000847                  -0.002352 -0.007995   \n","2017-08-01          -0.003318                  -0.009024 -0.009132   \n","\n","            Micro Finance News  International Trade News    EPU_US    EPU_UK  \n","Date                                                                          \n","2000-02-01            0.011022                  0.000489 -0.543803  0.082941  \n","2000-03-01           -0.019641                  0.018035 -0.621220  0.059239  \n","2000-04-01            0.004221                 -0.010737  0.454406  0.609795  \n","2000-05-01            0.004203                 -0.010853  0.675874 -0.899673  \n","2000-06-01            0.011326                  0.018424 -0.003626  1.055358  \n","...                        ...                       ...       ...       ...  \n","2017-04-01            0.001977                  0.011571  0.750910 -0.317777  \n","2017-05-01            0.024800                 -0.022444 -0.369826 -0.059514  \n","2017-06-01           -0.036240                  0.010546  0.185622  0.693738  \n","2017-07-01            0.014035                 -0.000650  0.012530 -0.552083  \n","2017-08-01            0.016215                 -0.009463 -0.113282 -0.194449  \n","\n","[211 rows x 16 columns]"],"text/html":["\n","  <div id=\"df-728949ab-93e5-4a4d-93e9-03f995f6726b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GBPUSD</th>\n","      <th>CPI_US</th>\n","      <th>CPI_UK</th>\n","      <th>Money Market Rate_US</th>\n","      <th>Money Market Rate_UK</th>\n","      <th>IPI_US</th>\n","      <th>IPI_UK</th>\n","      <th>M2_US</th>\n","      <th>M2_UK</th>\n","      <th>Stock Market News</th>\n","      <th>Economic Development News</th>\n","      <th>FED News</th>\n","      <th>Micro Finance News</th>\n","      <th>International Trade News</th>\n","      <th>EPU_US</th>\n","      <th>EPU_UK</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-02-01</th>\n","      <td>0.024422</td>\n","      <td>0.001826</td>\n","      <td>0.003095</td>\n","      <td>0.0006</td>\n","      <td>0.0010</td>\n","      <td>0.002880</td>\n","      <td>0.010610</td>\n","      <td>0.002831</td>\n","      <td>-0.019531</td>\n","      <td>0.004850</td>\n","      <td>-0.016937</td>\n","      <td>-0.003431</td>\n","      <td>0.011022</td>\n","      <td>0.000489</td>\n","      <td>-0.543803</td>\n","      <td>0.082941</td>\n","    </tr>\n","    <tr>\n","      <th>2000-03-01</th>\n","      <td>0.012355</td>\n","      <td>0.001823</td>\n","      <td>0.002478</td>\n","      <td>0.0010</td>\n","      <td>-0.0001</td>\n","      <td>0.003998</td>\n","      <td>-0.000880</td>\n","      <td>0.006511</td>\n","      <td>0.016098</td>\n","      <td>0.010032</td>\n","      <td>-0.010264</td>\n","      <td>-0.001292</td>\n","      <td>-0.019641</td>\n","      <td>0.018035</td>\n","      <td>-0.621220</td>\n","      <td>0.059239</td>\n","    </tr>\n","    <tr>\n","      <th>2000-04-01</th>\n","      <td>-0.001989</td>\n","      <td>0.000000</td>\n","      <td>0.003243</td>\n","      <td>0.0011</td>\n","      <td>0.0007</td>\n","      <td>0.007262</td>\n","      <td>-0.003527</td>\n","      <td>0.011845</td>\n","      <td>-0.019022</td>\n","      <td>-0.008960</td>\n","      <td>0.019750</td>\n","      <td>-0.002368</td>\n","      <td>0.004221</td>\n","      <td>-0.010737</td>\n","      <td>0.454406</td>\n","      <td>0.609795</td>\n","    </tr>\n","    <tr>\n","      <th>2000-05-01</th>\n","      <td>0.048292</td>\n","      <td>0.000607</td>\n","      <td>0.002725</td>\n","      <td>0.0045</td>\n","      <td>0.0000</td>\n","      <td>0.001844</td>\n","      <td>0.003527</td>\n","      <td>-0.002526</td>\n","      <td>-0.029046</td>\n","      <td>-0.009041</td>\n","      <td>0.019368</td>\n","      <td>-0.002373</td>\n","      <td>0.004203</td>\n","      <td>-0.010853</td>\n","      <td>0.675874</td>\n","      <td>-0.899673</td>\n","    </tr>\n","    <tr>\n","      <th>2000-06-01</th>\n","      <td>-0.000115</td>\n","      <td>0.006653</td>\n","      <td>0.001442</td>\n","      <td>0.0003</td>\n","      <td>-0.0007</td>\n","      <td>0.000912</td>\n","      <td>0.005268</td>\n","      <td>0.003766</td>\n","      <td>0.015023</td>\n","      <td>-0.002734</td>\n","      <td>-0.035576</td>\n","      <td>0.013077</td>\n","      <td>0.011326</td>\n","      <td>0.018424</td>\n","      <td>-0.003626</td>\n","      <td>1.055358</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2017-04-01</th>\n","      <td>-0.023466</td>\n","      <td>-0.001252</td>\n","      <td>0.004547</td>\n","      <td>0.0003</td>\n","      <td>-0.0002</td>\n","      <td>0.010821</td>\n","      <td>0.001967</td>\n","      <td>0.003895</td>\n","      <td>0.034527</td>\n","      <td>0.002005</td>\n","      <td>-0.006553</td>\n","      <td>-0.003000</td>\n","      <td>0.001977</td>\n","      <td>0.011571</td>\n","      <td>0.750910</td>\n","      <td>-0.317777</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-01</th>\n","      <td>-0.022458</td>\n","      <td>0.001135</td>\n","      <td>0.003491</td>\n","      <td>0.0003</td>\n","      <td>-0.0002</td>\n","      <td>-0.000316</td>\n","      <td>0.001963</td>\n","      <td>0.004828</td>\n","      <td>-0.002327</td>\n","      <td>0.000518</td>\n","      <td>-0.022231</td>\n","      <td>-0.018553</td>\n","      <td>0.024800</td>\n","      <td>-0.022444</td>\n","      <td>-0.369826</td>\n","      <td>-0.059514</td>\n","    </tr>\n","    <tr>\n","      <th>2017-06-01</th>\n","      <td>0.008963</td>\n","      <td>0.003480</td>\n","      <td>-0.000242</td>\n","      <td>0.0007</td>\n","      <td>-0.0002</td>\n","      <td>0.002219</td>\n","      <td>0.003914</td>\n","      <td>0.001936</td>\n","      <td>0.015077</td>\n","      <td>0.001780</td>\n","      <td>0.029919</td>\n","      <td>0.033112</td>\n","      <td>-0.036240</td>\n","      <td>0.010546</td>\n","      <td>0.185622</td>\n","      <td>0.693738</td>\n","    </tr>\n","    <tr>\n","      <th>2017-07-01</th>\n","      <td>-0.013999</td>\n","      <td>0.001899</td>\n","      <td>-0.000668</td>\n","      <td>0.0005</td>\n","      <td>0.0000</td>\n","      <td>-0.001361</td>\n","      <td>0.003899</td>\n","      <td>0.005625</td>\n","      <td>0.015743</td>\n","      <td>-0.000847</td>\n","      <td>-0.002352</td>\n","      <td>-0.007995</td>\n","      <td>0.014035</td>\n","      <td>-0.000650</td>\n","      <td>0.012530</td>\n","      <td>-0.552083</td>\n","    </tr>\n","    <tr>\n","      <th>2017-08-01</th>\n","      <td>0.003121</td>\n","      <td>0.002003</td>\n","      <td>0.005507</td>\n","      <td>0.0000</td>\n","      <td>-0.0001</td>\n","      <td>-0.007291</td>\n","      <td>0.000972</td>\n","      <td>0.003225</td>\n","      <td>-0.020494</td>\n","      <td>-0.003318</td>\n","      <td>-0.009024</td>\n","      <td>-0.009132</td>\n","      <td>0.016215</td>\n","      <td>-0.009463</td>\n","      <td>-0.113282</td>\n","      <td>-0.194449</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>211 rows × 16 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-728949ab-93e5-4a4d-93e9-03f995f6726b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-728949ab-93e5-4a4d-93e9-03f995f6726b button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-728949ab-93e5-4a4d-93e9-03f995f6726b');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-5c22b72d-876d-4729-bcf5-7986e7f8753b\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5c22b72d-876d-4729-bcf5-7986e7f8753b')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-5c22b72d-876d-4729-bcf5-7986e7f8753b button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"data","summary":"{\n  \"name\": \"data\",\n  \"rows\": 211,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2000-02-01 00:00:00\",\n        \"max\": \"2017-08-01 00:00:00\",\n        \"num_unique_values\": 211,\n        \"samples\": [\n          \"2002-08-01 00:00:00\",\n          \"2014-07-01 00:00:00\",\n          \"2011-10-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GBPUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021903039674555987,\n        \"min\": -0.058966413940568896,\n        \"max\": 0.09703159622393576,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.011393159398264796,\n          -0.009572377962586143,\n          0.0008932057891550826\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0030452458746710155,\n        \"min\": -0.017864094092818306,\n        \"max\": 0.013674561020173392,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          -0.0005849663811146044,\n          0.0017862463814637408,\n          0.004228600245438585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0035140390262740503,\n        \"min\": -0.0088780916994331,\n        \"max\": 0.009945929974087164,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.00287329511981671,\n          -0.003408895411168622,\n          0.0006988120480055926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0022478208811221295,\n        \"min\": -0.0178,\n        \"max\": 0.009399999999999995,\n        \"num_unique_values\": 116,\n        \"samples\": [\n          -0.0011999999999999997,\n          0.0003000000000000086,\n          -9.999999999999766e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0019111518691452773,\n        \"min\": -0.016799999999999995,\n        \"max\": 0.0040000000000000105,\n        \"num_unique_values\": 106,\n        \"samples\": [\n          -0.000599999999999999,\n          -0.0011000000000000038,\n          -0.0006999999999999923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006684355386162238,\n        \"min\": -0.044008207471526006,\n        \"max\": 0.014914905081914398,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.00032492066984435297,\n          0.00020951670450930493,\n          0.007079343191222165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00952707668395981,\n        \"min\": -0.04825697300777332,\n        \"max\": 0.025369194125459238,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          -0.008260717007366303,\n          0.0008948546458436013,\n          0.0090744724334062\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003761690427698376,\n        \"min\": -0.004561518312819146,\n        \"max\": 0.022217582227465726,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.0075589391463104505,\n          0.0052106295194391805,\n          0.003458687890089962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1637295133229077,\n        \"min\": -6.92642455773913,\n        \"max\": 7.001626503142007,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          -0.0031381110210766394,\n          -0.009285154360977543,\n          0.03318702403330853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Market News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011114492871743258,\n        \"min\": -0.04840344889516035,\n        \"max\": 0.062334576139732034,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          -0.006122164257193141,\n          0.02842697069904765,\n          -0.00403674016104949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economic Development News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011655893940569147,\n        \"min\": -0.03557577033031167,\n        \"max\": 0.029919311413495908,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.003063069986759537,\n          -0.008853670171760086,\n          -0.021085634692148192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FED News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011298308073668358,\n        \"min\": -0.02470761491829343,\n        \"max\": 0.0331117479536549,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.005575288953607815,\n          -0.017589757881970813,\n          -0.007258308296627991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro Finance News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02590033189827522,\n        \"min\": -0.08210857399910654,\n        \"max\": 0.056793254581466224,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.013929051813427407,\n          0.006864536966335155,\n          0.017304166832532664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"International Trade News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010995532421307946,\n        \"min\": -0.027712976353242214,\n        \"max\": 0.03900440702551222,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          -0.015780955301934796,\n          -0.008632660457603691,\n          0.007749628394848829\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5645373231907893,\n        \"min\": -1.8334616248313114,\n        \"max\": 1.502079241540831,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.21884889435386246,\n          0.7838847473287993,\n          -0.4650954530240643\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3110397840068799,\n        \"min\": -0.8996733444161249,\n        \"max\": 1.0553582936828247,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.05766166184304833,\n          -0.09834455523756969,\n          -0.024004546230261603\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":17}],"source":["#data = data.iloc[2:]\n","data = data.iloc[1:]\n","data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":410,"status":"ok","timestamp":1711360723448,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"1Jjo5-l6JZ0B","outputId":"c9137e9d-672f-4fb5-81ac-75c35953774a"},"outputs":[{"output_type":"stream","name":"stdout","text":["GBPUSD                        7\n","CPI_US                       12\n","CPI_UK                       12\n","Money Market Rate_US         41\n","Money Market Rate_UK         42\n","IPI_US                        7\n","IPI_UK                       14\n","M2_US                        13\n","M2_UK                        46\n","Stock Market News            18\n","Economic Development News     2\n","FED News                      1\n","Micro Finance News            1\n","International Trade News      2\n","EPU_US                        1\n","EPU_UK                        3\n","dtype: int64\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:11: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","<ipython-input-18-6cc41ab41369>:12: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n"]}],"source":["# Detect outliers and replace by nan\n","outliers = []\n","def detect_outliers_iqr(data):\n","  for column in data:\n","    q1 = data[column].quantile(0.25)\n","    q3 = data[column].quantile(0.75)\n","    #print(q1, q3)\n","    IQR = q3-q1\n","    lwr_bound = q1-(1.5*IQR)\n","    upr_bound = q3+(1.5*IQR)\n","    data[column] = data[column].mask(data[column] < q1 - 1.5 * IQR)\n","    data[column] = data[column].mask(data[column] > q3 + 1.5 * IQR)\n","detect_outliers_iqr(data)\n","#print(data)\n","print(data.isnull().sum()) # print how many outliers we have"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1711360723448,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"wl9IEuzfOmeI","outputId":"ca64df52-155b-42ac-a957-f4e36270e6b9"},"outputs":[{"output_type":"stream","name":"stdout","text":["GBPUSD                       0\n","CPI_US                       0\n","CPI_UK                       0\n","Money Market Rate_US         0\n","Money Market Rate_UK         0\n","IPI_US                       0\n","IPI_UK                       0\n","M2_US                        0\n","M2_UK                        0\n","Stock Market News            0\n","Economic Development News    0\n","FED News                     0\n","Micro Finance News           0\n","International Trade News     0\n","EPU_US                       0\n","EPU_UK                       0\n","dtype: int64\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n","<ipython-input-19-aa8c5292526e>:6: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  data[column].fillna(median, inplace=True)\n"]}],"source":["# Impute Outliers\n","for column in data:\n","  #mean = data[column].mean()\n","  #data[column].fillna(mean, inplace=True)\n","  median = data[column].median()\n","  data[column].fillna(median, inplace=True)\n","  #print(mean)\n","print(data.isnull().sum())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1711360723449,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"XcVBZMf3tt0H","outputId":"eb1571f9-0850-4747-d042-c21194595686"},"outputs":[{"output_type":"stream","name":"stdout","text":["GBPUSD\n","3.195885974689677e-18\n","CPI_US\n","3.5022319410523443e-07\n","CPI_UK\n","0.08658238158837195\n","Money Market Rate_US\n","0.0070076705517349465\n","Money Market Rate_UK\n","2.777539860806871e-10\n","IPI_US\n","2.621032617088346e-07\n","IPI_UK\n","2.153643129942898e-30\n","M2_US\n","1.360520046495922e-12\n","M2_UK\n","7.474982369231475e-07\n","Stock Market News\n","8.493930772607713e-25\n","Economic Development News\n","0.00016433684900068142\n","FED News\n","0.00043924276368327776\n","Micro Finance News\n","0.005602860403180456\n","International Trade News\n","0.0006121354259344973\n","EPU_US\n","3.362092877603434e-17\n","EPU_UK\n","1.5692845900489245e-20\n"]}],"source":["# Check if time series are stationary after first differencing\n","from statsmodels.tsa.stattools import adfuller\n","for column in data:\n","  print(column)\n","  print(adfuller(data[column])[1])\n","  #P value is always <0.1-> stationary"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"elapsed":366,"status":"ok","timestamp":1711360723812,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"kAlRGBOASX2x","outputId":"0e7b39e9-b059-4dd3-b699-6fb9dddef221"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  \\\n","Date                                                             \n","2000-02-01  0.024422  0.001826  0.003095                0.0006   \n","2000-03-01  0.012355  0.001823  0.002478                0.0010   \n","2000-04-01 -0.001989  0.000000  0.003243                0.0011   \n","2000-05-01  0.048292  0.000607  0.002725                0.0000   \n","2000-06-01 -0.000115  0.006653  0.001442                0.0003   \n","...              ...       ...       ...                   ...   \n","2017-04-01 -0.023466 -0.001252  0.004547                0.0003   \n","2017-05-01 -0.022458  0.001135  0.003491                0.0003   \n","2017-06-01  0.008963  0.003480 -0.000242                0.0007   \n","2017-07-01 -0.013999  0.001899 -0.000668                0.0005   \n","2017-08-01  0.003121  0.002003  0.005507                0.0000   \n","\n","            Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  \\\n","Date                                                                       \n","2000-02-01                0.0010  0.002880  0.010610  0.002831 -0.019531   \n","2000-03-01               -0.0001  0.003998 -0.000880  0.006511  0.016098   \n","2000-04-01                0.0007  0.007262 -0.003527  0.004809 -0.019022   \n","2000-05-01                0.0000  0.001844  0.003527  0.004809 -0.029046   \n","2000-06-01               -0.0007  0.000912  0.005268  0.003766  0.015023   \n","...                          ...       ...       ...       ...       ...   \n","2017-04-01               -0.0002  0.010821  0.001967  0.003895  0.034527   \n","2017-05-01               -0.0002 -0.000316  0.001963  0.004828 -0.002327   \n","2017-06-01               -0.0002  0.002219  0.003914  0.001936  0.015077   \n","2017-07-01                0.0000 -0.001361  0.003899  0.005625  0.015743   \n","2017-08-01               -0.0001 -0.007291  0.000972  0.003225 -0.020494   \n","\n","            Stock Market News  Economic Development News  FED News  \\\n","Date                                                                 \n","2000-02-01           0.004850                  -0.016937 -0.003431   \n","2000-03-01           0.010032                  -0.010264 -0.001292   \n","2000-04-01          -0.008960                   0.019750 -0.002368   \n","2000-05-01          -0.009041                   0.019368 -0.002373   \n","2000-06-01          -0.002734                  -0.001099  0.013077   \n","...                       ...                        ...       ...   \n","2017-04-01           0.002005                  -0.006553 -0.003000   \n","2017-05-01           0.000518                  -0.022231 -0.018553   \n","2017-06-01           0.001780                  -0.001099 -0.000859   \n","2017-07-01          -0.000847                  -0.002352 -0.007995   \n","2017-08-01          -0.003318                  -0.009024 -0.009132   \n","\n","            Micro Finance News  International Trade News    EPU_US    EPU_UK  \n","Date                                                                          \n","2000-02-01            0.011022                  0.000489 -0.543803  0.082941  \n","2000-03-01           -0.019641                  0.018035 -0.621220  0.059239  \n","2000-04-01            0.004221                 -0.010737  0.454406  0.609795  \n","2000-05-01            0.004203                 -0.010853  0.675874  0.020484  \n","2000-06-01            0.011326                  0.018424 -0.003626  0.020484  \n","...                        ...                       ...       ...       ...  \n","2017-04-01            0.001977                  0.011571  0.750910 -0.317777  \n","2017-05-01            0.024800                 -0.022444 -0.369826 -0.059514  \n","2017-06-01           -0.036240                  0.010546  0.185622  0.693738  \n","2017-07-01            0.014035                 -0.000650  0.012530 -0.552083  \n","2017-08-01            0.016215                 -0.009463 -0.113282 -0.194449  \n","\n","[211 rows x 16 columns]"],"text/html":["\n","  <div id=\"df-3a04be3a-78ad-4ab0-a2be-a9c43c27d69b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GBPUSD</th>\n","      <th>CPI_US</th>\n","      <th>CPI_UK</th>\n","      <th>Money Market Rate_US</th>\n","      <th>Money Market Rate_UK</th>\n","      <th>IPI_US</th>\n","      <th>IPI_UK</th>\n","      <th>M2_US</th>\n","      <th>M2_UK</th>\n","      <th>Stock Market News</th>\n","      <th>Economic Development News</th>\n","      <th>FED News</th>\n","      <th>Micro Finance News</th>\n","      <th>International Trade News</th>\n","      <th>EPU_US</th>\n","      <th>EPU_UK</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-02-01</th>\n","      <td>0.024422</td>\n","      <td>0.001826</td>\n","      <td>0.003095</td>\n","      <td>0.0006</td>\n","      <td>0.0010</td>\n","      <td>0.002880</td>\n","      <td>0.010610</td>\n","      <td>0.002831</td>\n","      <td>-0.019531</td>\n","      <td>0.004850</td>\n","      <td>-0.016937</td>\n","      <td>-0.003431</td>\n","      <td>0.011022</td>\n","      <td>0.000489</td>\n","      <td>-0.543803</td>\n","      <td>0.082941</td>\n","    </tr>\n","    <tr>\n","      <th>2000-03-01</th>\n","      <td>0.012355</td>\n","      <td>0.001823</td>\n","      <td>0.002478</td>\n","      <td>0.0010</td>\n","      <td>-0.0001</td>\n","      <td>0.003998</td>\n","      <td>-0.000880</td>\n","      <td>0.006511</td>\n","      <td>0.016098</td>\n","      <td>0.010032</td>\n","      <td>-0.010264</td>\n","      <td>-0.001292</td>\n","      <td>-0.019641</td>\n","      <td>0.018035</td>\n","      <td>-0.621220</td>\n","      <td>0.059239</td>\n","    </tr>\n","    <tr>\n","      <th>2000-04-01</th>\n","      <td>-0.001989</td>\n","      <td>0.000000</td>\n","      <td>0.003243</td>\n","      <td>0.0011</td>\n","      <td>0.0007</td>\n","      <td>0.007262</td>\n","      <td>-0.003527</td>\n","      <td>0.004809</td>\n","      <td>-0.019022</td>\n","      <td>-0.008960</td>\n","      <td>0.019750</td>\n","      <td>-0.002368</td>\n","      <td>0.004221</td>\n","      <td>-0.010737</td>\n","      <td>0.454406</td>\n","      <td>0.609795</td>\n","    </tr>\n","    <tr>\n","      <th>2000-05-01</th>\n","      <td>0.048292</td>\n","      <td>0.000607</td>\n","      <td>0.002725</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.001844</td>\n","      <td>0.003527</td>\n","      <td>0.004809</td>\n","      <td>-0.029046</td>\n","      <td>-0.009041</td>\n","      <td>0.019368</td>\n","      <td>-0.002373</td>\n","      <td>0.004203</td>\n","      <td>-0.010853</td>\n","      <td>0.675874</td>\n","      <td>0.020484</td>\n","    </tr>\n","    <tr>\n","      <th>2000-06-01</th>\n","      <td>-0.000115</td>\n","      <td>0.006653</td>\n","      <td>0.001442</td>\n","      <td>0.0003</td>\n","      <td>-0.0007</td>\n","      <td>0.000912</td>\n","      <td>0.005268</td>\n","      <td>0.003766</td>\n","      <td>0.015023</td>\n","      <td>-0.002734</td>\n","      <td>-0.001099</td>\n","      <td>0.013077</td>\n","      <td>0.011326</td>\n","      <td>0.018424</td>\n","      <td>-0.003626</td>\n","      <td>0.020484</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2017-04-01</th>\n","      <td>-0.023466</td>\n","      <td>-0.001252</td>\n","      <td>0.004547</td>\n","      <td>0.0003</td>\n","      <td>-0.0002</td>\n","      <td>0.010821</td>\n","      <td>0.001967</td>\n","      <td>0.003895</td>\n","      <td>0.034527</td>\n","      <td>0.002005</td>\n","      <td>-0.006553</td>\n","      <td>-0.003000</td>\n","      <td>0.001977</td>\n","      <td>0.011571</td>\n","      <td>0.750910</td>\n","      <td>-0.317777</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-01</th>\n","      <td>-0.022458</td>\n","      <td>0.001135</td>\n","      <td>0.003491</td>\n","      <td>0.0003</td>\n","      <td>-0.0002</td>\n","      <td>-0.000316</td>\n","      <td>0.001963</td>\n","      <td>0.004828</td>\n","      <td>-0.002327</td>\n","      <td>0.000518</td>\n","      <td>-0.022231</td>\n","      <td>-0.018553</td>\n","      <td>0.024800</td>\n","      <td>-0.022444</td>\n","      <td>-0.369826</td>\n","      <td>-0.059514</td>\n","    </tr>\n","    <tr>\n","      <th>2017-06-01</th>\n","      <td>0.008963</td>\n","      <td>0.003480</td>\n","      <td>-0.000242</td>\n","      <td>0.0007</td>\n","      <td>-0.0002</td>\n","      <td>0.002219</td>\n","      <td>0.003914</td>\n","      <td>0.001936</td>\n","      <td>0.015077</td>\n","      <td>0.001780</td>\n","      <td>-0.001099</td>\n","      <td>-0.000859</td>\n","      <td>-0.036240</td>\n","      <td>0.010546</td>\n","      <td>0.185622</td>\n","      <td>0.693738</td>\n","    </tr>\n","    <tr>\n","      <th>2017-07-01</th>\n","      <td>-0.013999</td>\n","      <td>0.001899</td>\n","      <td>-0.000668</td>\n","      <td>0.0005</td>\n","      <td>0.0000</td>\n","      <td>-0.001361</td>\n","      <td>0.003899</td>\n","      <td>0.005625</td>\n","      <td>0.015743</td>\n","      <td>-0.000847</td>\n","      <td>-0.002352</td>\n","      <td>-0.007995</td>\n","      <td>0.014035</td>\n","      <td>-0.000650</td>\n","      <td>0.012530</td>\n","      <td>-0.552083</td>\n","    </tr>\n","    <tr>\n","      <th>2017-08-01</th>\n","      <td>0.003121</td>\n","      <td>0.002003</td>\n","      <td>0.005507</td>\n","      <td>0.0000</td>\n","      <td>-0.0001</td>\n","      <td>-0.007291</td>\n","      <td>0.000972</td>\n","      <td>0.003225</td>\n","      <td>-0.020494</td>\n","      <td>-0.003318</td>\n","      <td>-0.009024</td>\n","      <td>-0.009132</td>\n","      <td>0.016215</td>\n","      <td>-0.009463</td>\n","      <td>-0.113282</td>\n","      <td>-0.194449</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>211 rows × 16 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a04be3a-78ad-4ab0-a2be-a9c43c27d69b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-3a04be3a-78ad-4ab0-a2be-a9c43c27d69b button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-3a04be3a-78ad-4ab0-a2be-a9c43c27d69b');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-4a92df09-81d7-49aa-a9e4-94aca167a921\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a92df09-81d7-49aa-a9e4-94aca167a921')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-4a92df09-81d7-49aa-a9e4-94aca167a921 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"data","summary":"{\n  \"name\": \"data\",\n  \"rows\": 211,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2000-02-01 00:00:00\",\n        \"max\": \"2017-08-01 00:00:00\",\n        \"num_unique_values\": 211,\n        \"samples\": [\n          \"2002-08-01 00:00:00\",\n          \"2014-07-01 00:00:00\",\n          \"2011-10-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GBPUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0180336206920251,\n        \"min\": -0.047321236497454955,\n        \"max\": 0.048292449036688834,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          0.00626873527627686,\n          0.01775775212210573,\n          -0.00032398851060277956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0019860516843859346,\n        \"min\": -0.0034980433127662636,\n        \"max\": 0.0071160653935047335,\n        \"num_unique_values\": 193,\n        \"samples\": [\n          0.0016515280384732378,\n          0.0006749796769751626,\n          0.006138126688785661\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0027544535595781625,\n        \"min\": -0.005412838244263085,\n        \"max\": 0.008622209424218319,\n        \"num_unique_values\": 199,\n        \"samples\": [\n          0.004718239231797483,\n          0.007598408335619311,\n          0.0028045692767024732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005152939664038696,\n        \"min\": -0.0011999999999999997,\n        \"max\": 0.0016000000000000042,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.001299999999999999,\n          0.0005999999999999964,\n          -0.00020000000000000052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00039202556579106824,\n        \"min\": -0.0011000000000000038,\n        \"max\": 0.0011000000000000038,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          0.0005999999999999964,\n          -0.0001999999999999988,\n          -0.0006999999999999923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0047444669235660155,\n        \"min\": -0.0123574544234204,\n        \"max\": 0.012444581674208699,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          -0.006686408110025077,\n          0.0001732219708232563,\n          -0.0016441322076081732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006530644761844374,\n        \"min\": -0.019082814416440996,\n        \"max\": 0.017873576799295243,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          0.0029455102297566427,\n          0.009004563093082396,\n          0.00704582683320254\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0025069729258653706,\n        \"min\": -0.001519631616664796,\n        \"max\": 0.011447093338732728,\n        \"num_unique_values\": 199,\n        \"samples\": [\n          0.004525831135353542,\n          0.005736651977434448,\n          0.00613745051450465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020927858635025776,\n        \"min\": -0.07699919034517144,\n        \"max\": 0.06632646600677461,\n        \"num_unique_values\": 165,\n        \"samples\": [\n          -0.00153591559980093,\n          0.014289328122696787,\n          0.008810761518249777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Market News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0064429194335870405,\n        \"min\": -0.01859941445383617,\n        \"max\": 0.014544588069430597,\n        \"num_unique_values\": 193,\n        \"samples\": [\n          0.007467441648361328,\n          0.000851083871776348,\n          -0.002305885236704164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economic Development News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011206565539716152,\n        \"min\": -0.030415123871464278,\n        \"max\": 0.027622579719468243,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          0.003063069986759537,\n          0.025436159456551533,\n          -0.016918122761857113\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FED News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01106498281121609,\n        \"min\": -0.02470761491829343,\n        \"max\": 0.024982789917815662,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.005575288953607815,\n          -0.017589757881970813,\n          -0.007258308296627991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro Finance News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025267632529778716,\n        \"min\": -0.06490524626209426,\n        \"max\": 0.056793254581466224,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.013929051813427407,\n          0.006864536966335155,\n          0.017304166832532664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"International Trade News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01043897164159676,\n        \"min\": -0.027712976353242214,\n        \"max\": 0.027951922151420616,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          -0.015780955301934796,\n          0.007163775734450706,\n          0.005218257713128338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5500682887320152,\n        \"min\": -1.2535877108101587,\n        \"max\": 1.502079241540831,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.21884889435386246,\n          0.7838847473287993,\n          -0.4650954530240643\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2899108107474186,\n        \"min\": -0.6565847190764948,\n        \"max\": 0.7538511306806992,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          0.30597803027575665,\n          -0.09834455523756969,\n          -0.2342250465215221\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":21}],"source":["# First Differencing\n","#data[\"Economic Development News\"] = data[\"Economic Development News\"].diff()\n","data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sBJMYKsgTVrB"},"outputs":[],"source":["#data = data.iloc[1:]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1711360723812,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"lcHSqon-mw0G","outputId":"73aa006a-9ee8-4dfe-8499-a950f4001c2d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  \\\n","Date                                                             \n","2000-02-01  0.024422  0.001826  0.003095                0.0006   \n","2000-03-01  0.012355  0.001823  0.002478                0.0010   \n","2000-04-01 -0.001989  0.000000  0.003243                0.0011   \n","2000-05-01  0.048292  0.000607  0.002725                0.0000   \n","2000-06-01 -0.000115  0.006653  0.001442                0.0003   \n","...              ...       ...       ...                   ...   \n","2017-04-01 -0.023466 -0.001252  0.004547                0.0003   \n","2017-05-01 -0.022458  0.001135  0.003491                0.0003   \n","2017-06-01  0.008963  0.003480 -0.000242                0.0007   \n","2017-07-01 -0.013999  0.001899 -0.000668                0.0005   \n","2017-08-01  0.003121  0.002003  0.005507                0.0000   \n","\n","            Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  \\\n","Date                                                                       \n","2000-02-01                0.0010  0.002880  0.010610  0.002831 -0.019531   \n","2000-03-01               -0.0001  0.003998 -0.000880  0.006511  0.016098   \n","2000-04-01                0.0007  0.007262 -0.003527  0.004809 -0.019022   \n","2000-05-01                0.0000  0.001844  0.003527  0.004809 -0.029046   \n","2000-06-01               -0.0007  0.000912  0.005268  0.003766  0.015023   \n","...                          ...       ...       ...       ...       ...   \n","2017-04-01               -0.0002  0.010821  0.001967  0.003895  0.034527   \n","2017-05-01               -0.0002 -0.000316  0.001963  0.004828 -0.002327   \n","2017-06-01               -0.0002  0.002219  0.003914  0.001936  0.015077   \n","2017-07-01                0.0000 -0.001361  0.003899  0.005625  0.015743   \n","2017-08-01               -0.0001 -0.007291  0.000972  0.003225 -0.020494   \n","\n","            Stock Market News  Economic Development News  FED News  \\\n","Date                                                                 \n","2000-02-01           0.004850                  -0.016937 -0.003431   \n","2000-03-01           0.010032                  -0.010264 -0.001292   \n","2000-04-01          -0.008960                   0.019750 -0.002368   \n","2000-05-01          -0.009041                   0.019368 -0.002373   \n","2000-06-01          -0.002734                  -0.001099  0.013077   \n","...                       ...                        ...       ...   \n","2017-04-01           0.002005                  -0.006553 -0.003000   \n","2017-05-01           0.000518                  -0.022231 -0.018553   \n","2017-06-01           0.001780                  -0.001099 -0.000859   \n","2017-07-01          -0.000847                  -0.002352 -0.007995   \n","2017-08-01          -0.003318                  -0.009024 -0.009132   \n","\n","            Micro Finance News  International Trade News    EPU_US    EPU_UK  \n","Date                                                                          \n","2000-02-01            0.011022                  0.000489 -0.543803  0.082941  \n","2000-03-01           -0.019641                  0.018035 -0.621220  0.059239  \n","2000-04-01            0.004221                 -0.010737  0.454406  0.609795  \n","2000-05-01            0.004203                 -0.010853  0.675874  0.020484  \n","2000-06-01            0.011326                  0.018424 -0.003626  0.020484  \n","...                        ...                       ...       ...       ...  \n","2017-04-01            0.001977                  0.011571  0.750910 -0.317777  \n","2017-05-01            0.024800                 -0.022444 -0.369826 -0.059514  \n","2017-06-01           -0.036240                  0.010546  0.185622  0.693738  \n","2017-07-01            0.014035                 -0.000650  0.012530 -0.552083  \n","2017-08-01            0.016215                 -0.009463 -0.113282 -0.194449  \n","\n","[211 rows x 16 columns]"],"text/html":["\n","  <div id=\"df-0103ed7a-fa06-4120-9b22-25a42cb335f5\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GBPUSD</th>\n","      <th>CPI_US</th>\n","      <th>CPI_UK</th>\n","      <th>Money Market Rate_US</th>\n","      <th>Money Market Rate_UK</th>\n","      <th>IPI_US</th>\n","      <th>IPI_UK</th>\n","      <th>M2_US</th>\n","      <th>M2_UK</th>\n","      <th>Stock Market News</th>\n","      <th>Economic Development News</th>\n","      <th>FED News</th>\n","      <th>Micro Finance News</th>\n","      <th>International Trade News</th>\n","      <th>EPU_US</th>\n","      <th>EPU_UK</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-02-01</th>\n","      <td>0.024422</td>\n","      <td>0.001826</td>\n","      <td>0.003095</td>\n","      <td>0.0006</td>\n","      <td>0.0010</td>\n","      <td>0.002880</td>\n","      <td>0.010610</td>\n","      <td>0.002831</td>\n","      <td>-0.019531</td>\n","      <td>0.004850</td>\n","      <td>-0.016937</td>\n","      <td>-0.003431</td>\n","      <td>0.011022</td>\n","      <td>0.000489</td>\n","      <td>-0.543803</td>\n","      <td>0.082941</td>\n","    </tr>\n","    <tr>\n","      <th>2000-03-01</th>\n","      <td>0.012355</td>\n","      <td>0.001823</td>\n","      <td>0.002478</td>\n","      <td>0.0010</td>\n","      <td>-0.0001</td>\n","      <td>0.003998</td>\n","      <td>-0.000880</td>\n","      <td>0.006511</td>\n","      <td>0.016098</td>\n","      <td>0.010032</td>\n","      <td>-0.010264</td>\n","      <td>-0.001292</td>\n","      <td>-0.019641</td>\n","      <td>0.018035</td>\n","      <td>-0.621220</td>\n","      <td>0.059239</td>\n","    </tr>\n","    <tr>\n","      <th>2000-04-01</th>\n","      <td>-0.001989</td>\n","      <td>0.000000</td>\n","      <td>0.003243</td>\n","      <td>0.0011</td>\n","      <td>0.0007</td>\n","      <td>0.007262</td>\n","      <td>-0.003527</td>\n","      <td>0.004809</td>\n","      <td>-0.019022</td>\n","      <td>-0.008960</td>\n","      <td>0.019750</td>\n","      <td>-0.002368</td>\n","      <td>0.004221</td>\n","      <td>-0.010737</td>\n","      <td>0.454406</td>\n","      <td>0.609795</td>\n","    </tr>\n","    <tr>\n","      <th>2000-05-01</th>\n","      <td>0.048292</td>\n","      <td>0.000607</td>\n","      <td>0.002725</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.001844</td>\n","      <td>0.003527</td>\n","      <td>0.004809</td>\n","      <td>-0.029046</td>\n","      <td>-0.009041</td>\n","      <td>0.019368</td>\n","      <td>-0.002373</td>\n","      <td>0.004203</td>\n","      <td>-0.010853</td>\n","      <td>0.675874</td>\n","      <td>0.020484</td>\n","    </tr>\n","    <tr>\n","      <th>2000-06-01</th>\n","      <td>-0.000115</td>\n","      <td>0.006653</td>\n","      <td>0.001442</td>\n","      <td>0.0003</td>\n","      <td>-0.0007</td>\n","      <td>0.000912</td>\n","      <td>0.005268</td>\n","      <td>0.003766</td>\n","      <td>0.015023</td>\n","      <td>-0.002734</td>\n","      <td>-0.001099</td>\n","      <td>0.013077</td>\n","      <td>0.011326</td>\n","      <td>0.018424</td>\n","      <td>-0.003626</td>\n","      <td>0.020484</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2017-04-01</th>\n","      <td>-0.023466</td>\n","      <td>-0.001252</td>\n","      <td>0.004547</td>\n","      <td>0.0003</td>\n","      <td>-0.0002</td>\n","      <td>0.010821</td>\n","      <td>0.001967</td>\n","      <td>0.003895</td>\n","      <td>0.034527</td>\n","      <td>0.002005</td>\n","      <td>-0.006553</td>\n","      <td>-0.003000</td>\n","      <td>0.001977</td>\n","      <td>0.011571</td>\n","      <td>0.750910</td>\n","      <td>-0.317777</td>\n","    </tr>\n","    <tr>\n","      <th>2017-05-01</th>\n","      <td>-0.022458</td>\n","      <td>0.001135</td>\n","      <td>0.003491</td>\n","      <td>0.0003</td>\n","      <td>-0.0002</td>\n","      <td>-0.000316</td>\n","      <td>0.001963</td>\n","      <td>0.004828</td>\n","      <td>-0.002327</td>\n","      <td>0.000518</td>\n","      <td>-0.022231</td>\n","      <td>-0.018553</td>\n","      <td>0.024800</td>\n","      <td>-0.022444</td>\n","      <td>-0.369826</td>\n","      <td>-0.059514</td>\n","    </tr>\n","    <tr>\n","      <th>2017-06-01</th>\n","      <td>0.008963</td>\n","      <td>0.003480</td>\n","      <td>-0.000242</td>\n","      <td>0.0007</td>\n","      <td>-0.0002</td>\n","      <td>0.002219</td>\n","      <td>0.003914</td>\n","      <td>0.001936</td>\n","      <td>0.015077</td>\n","      <td>0.001780</td>\n","      <td>-0.001099</td>\n","      <td>-0.000859</td>\n","      <td>-0.036240</td>\n","      <td>0.010546</td>\n","      <td>0.185622</td>\n","      <td>0.693738</td>\n","    </tr>\n","    <tr>\n","      <th>2017-07-01</th>\n","      <td>-0.013999</td>\n","      <td>0.001899</td>\n","      <td>-0.000668</td>\n","      <td>0.0005</td>\n","      <td>0.0000</td>\n","      <td>-0.001361</td>\n","      <td>0.003899</td>\n","      <td>0.005625</td>\n","      <td>0.015743</td>\n","      <td>-0.000847</td>\n","      <td>-0.002352</td>\n","      <td>-0.007995</td>\n","      <td>0.014035</td>\n","      <td>-0.000650</td>\n","      <td>0.012530</td>\n","      <td>-0.552083</td>\n","    </tr>\n","    <tr>\n","      <th>2017-08-01</th>\n","      <td>0.003121</td>\n","      <td>0.002003</td>\n","      <td>0.005507</td>\n","      <td>0.0000</td>\n","      <td>-0.0001</td>\n","      <td>-0.007291</td>\n","      <td>0.000972</td>\n","      <td>0.003225</td>\n","      <td>-0.020494</td>\n","      <td>-0.003318</td>\n","      <td>-0.009024</td>\n","      <td>-0.009132</td>\n","      <td>0.016215</td>\n","      <td>-0.009463</td>\n","      <td>-0.113282</td>\n","      <td>-0.194449</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>211 rows × 16 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0103ed7a-fa06-4120-9b22-25a42cb335f5')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-0103ed7a-fa06-4120-9b22-25a42cb335f5 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-0103ed7a-fa06-4120-9b22-25a42cb335f5');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-7522d4ff-f2ed-4b0f-b3cc-56f4bf745ae7\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7522d4ff-f2ed-4b0f-b3cc-56f4bf745ae7')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-7522d4ff-f2ed-4b0f-b3cc-56f4bf745ae7 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"data","summary":"{\n  \"name\": \"data\",\n  \"rows\": 211,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2000-02-01 00:00:00\",\n        \"max\": \"2017-08-01 00:00:00\",\n        \"num_unique_values\": 211,\n        \"samples\": [\n          \"2002-08-01 00:00:00\",\n          \"2014-07-01 00:00:00\",\n          \"2011-10-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GBPUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0180336206920251,\n        \"min\": -0.047321236497454955,\n        \"max\": 0.048292449036688834,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          0.00626873527627686,\n          0.01775775212210573,\n          -0.00032398851060277956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0019860516843859346,\n        \"min\": -0.0034980433127662636,\n        \"max\": 0.0071160653935047335,\n        \"num_unique_values\": 193,\n        \"samples\": [\n          0.0016515280384732378,\n          0.0006749796769751626,\n          0.006138126688785661\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0027544535595781625,\n        \"min\": -0.005412838244263085,\n        \"max\": 0.008622209424218319,\n        \"num_unique_values\": 199,\n        \"samples\": [\n          0.004718239231797483,\n          0.007598408335619311,\n          0.0028045692767024732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005152939664038696,\n        \"min\": -0.0011999999999999997,\n        \"max\": 0.0016000000000000042,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.001299999999999999,\n          0.0005999999999999964,\n          -0.00020000000000000052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00039202556579106824,\n        \"min\": -0.0011000000000000038,\n        \"max\": 0.0011000000000000038,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          0.0005999999999999964,\n          -0.0001999999999999988,\n          -0.0006999999999999923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0047444669235660155,\n        \"min\": -0.0123574544234204,\n        \"max\": 0.012444581674208699,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          -0.006686408110025077,\n          0.0001732219708232563,\n          -0.0016441322076081732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006530644761844374,\n        \"min\": -0.019082814416440996,\n        \"max\": 0.017873576799295243,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          0.0029455102297566427,\n          0.009004563093082396,\n          0.00704582683320254\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0025069729258653706,\n        \"min\": -0.001519631616664796,\n        \"max\": 0.011447093338732728,\n        \"num_unique_values\": 199,\n        \"samples\": [\n          0.004525831135353542,\n          0.005736651977434448,\n          0.00613745051450465\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020927858635025776,\n        \"min\": -0.07699919034517144,\n        \"max\": 0.06632646600677461,\n        \"num_unique_values\": 165,\n        \"samples\": [\n          -0.00153591559980093,\n          0.014289328122696787,\n          0.008810761518249777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Market News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0064429194335870405,\n        \"min\": -0.01859941445383617,\n        \"max\": 0.014544588069430597,\n        \"num_unique_values\": 193,\n        \"samples\": [\n          0.007467441648361328,\n          0.000851083871776348,\n          -0.002305885236704164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economic Development News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011206565539716152,\n        \"min\": -0.030415123871464278,\n        \"max\": 0.027622579719468243,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          0.003063069986759537,\n          0.025436159456551533,\n          -0.016918122761857113\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FED News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01106498281121609,\n        \"min\": -0.02470761491829343,\n        \"max\": 0.024982789917815662,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.005575288953607815,\n          -0.017589757881970813,\n          -0.007258308296627991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro Finance News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025267632529778716,\n        \"min\": -0.06490524626209426,\n        \"max\": 0.056793254581466224,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.013929051813427407,\n          0.006864536966335155,\n          0.017304166832532664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"International Trade News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01043897164159676,\n        \"min\": -0.027712976353242214,\n        \"max\": 0.027951922151420616,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          -0.015780955301934796,\n          0.007163775734450706,\n          0.005218257713128338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5500682887320152,\n        \"min\": -1.2535877108101587,\n        \"max\": 1.502079241540831,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          0.21884889435386246,\n          0.7838847473287993,\n          -0.4650954530240643\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2899108107474186,\n        \"min\": -0.6565847190764948,\n        \"max\": 0.7538511306806992,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          0.30597803027575665,\n          -0.09834455523756969,\n          -0.2342250465215221\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":23}],"source":["# final data\n","data"]},{"cell_type":"markdown","source":["# Robustness test for my news variables"],"metadata":{"id":"Yar_NN8vFDoG"}},{"cell_type":"code","source":["# Robustness test for my news variables\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","# Define your entropy variables\n","entropy_variables = ['Stock Market News', 'Economic Development News', 'FED News',\n","                     'Micro Finance News', 'International Trade News', 'EPU_US', 'EPU_UK']\n","\n","# Define colors for plotting\n","colors = [\"#6D2077\", \"#00A3A1\", \"#ff9933\", \"#BC204B\", \"#333333\"]\n","\n","# Iterate over each entropy variable and its corresponding color\n","for variable, color in zip(entropy_variables, colors):\n","    # Calculate the rolling mean with a window size of 30 (adjust as needed)\n","    rolling_mean = data[variable].rolling(window=30).mean()\n","\n","    # Plot the original data and the rolling mean\n","    plt.figure(figsize=(10, 6))\n","    plt.plot(data.index, data[variable], label='Original Data', color=color)\n","    plt.plot(data.index, rolling_mean, label='Rolling Mean', color='black', linestyle='--')\n","    plt.title(f'Rolling Mean of {variable}')\n","    plt.xlabel('Date')\n","    plt.ylabel(f\"Log {variable}\")  # Assuming data is logarithmically transformed\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"287eZsp2l5BD","executionInfo":{"status":"ok","timestamp":1711360727172,"user_tz":-60,"elapsed":3365,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"38ab78a4-54c5-4256-e91c-ab7ed83ea761"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","\n","def robustness_test_function(data, window_size):\n","    rolling_std = data.rolling(window=window_size).std()\n","    average_rolling_std = rolling_std.mean()\n","    print(average_rolling_std)\n","    threshold = 0.1  # Adjust as needed\n","    not_robust_vars = data.columns[average_rolling_std > threshold].tolist()\n","    if not_robust_vars:\n","        message = f\"Warning: {', '.join(not_robust_vars)} may not be robust.\"\n","    else:\n","        message = \"All entropy variables appear to be robust.\"\n","    return message, not_robust_vars, average_rolling_std\n","\n","entropy_variables = ['Stock Market News', 'Economic Development News', 'FED News',\n","                     'Micro Finance News', 'International Trade News', 'EPU_US', 'EPU_UK']\n","\n","colors = [\"#6D2077\", \"#00A3A1\", \"#ff9933\", \"#BC204B\", \"#333333\"]\n","\n","results = {}\n","\n","# Initialize a dictionary to store average rolling standard deviation values\n","avg_std_dict = {}\n","\n","for variable, color in zip(entropy_variables, colors):\n","    message, not_robust_vars, avg_std = robustness_test_function(data[[variable]], window_size=30)\n","    results[variable] = {\"message\": message, \"not_robust_vars\": not_robust_vars}\n","\n","    # Save the average rolling standard deviation values to the dictionary\n","    avg_std_dict[variable] = avg_std\n","\n","    # Print the result including average rolling standard deviation\n","    print(f\"{variable}: {message}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JaWkWjNvoqFa","executionInfo":{"status":"ok","timestamp":1711360727172,"user_tz":-60,"elapsed":3,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"86b7e0a5-3b29-48bc-d5b7-e397afad03d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Stock Market News    0.006262\n","dtype: float64\n","Stock Market News: All entropy variables appear to be robust.\n","Economic Development News    0.011\n","dtype: float64\n","Economic Development News: All entropy variables appear to be robust.\n","FED News    0.011068\n","dtype: float64\n","FED News: All entropy variables appear to be robust.\n","Micro Finance News    0.025864\n","dtype: float64\n","Micro Finance News: All entropy variables appear to be robust.\n","International Trade News    0.010236\n","dtype: float64\n","International Trade News: All entropy variables appear to be robust.\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# Define your entropy variables\n","entropy_variables = ['Stock Market News', 'Economic Development News', 'FED News',\n","                     'Micro Finance News', 'International Trade News']\n","                     #'EPU_US', 'EPU_UK'\n","\n","# Define colors for plotting\n","colors = [\"#6D2077\", \"#00A3A1\", \"#ff9933\", \"#BC204B\", \"#333333\"]\n","plt.figure(figsize=(6, 4))\n","# Plot rolling standard deviation for each entropy variable\n","plt.figure(figsize=(10, 6))  # Adjust figure size as needed\n","\n","for i, variable in enumerate(entropy_variables):\n","    rolling_std = data[variable].rolling(window=30).std()\n","    plt.plot(rolling_std, label=variable, color=colors[i % len(colors)])\n","# Add y=0.1 line\n","plt.axhline(y=0.1, color='red', linestyle='--', label='Threshold')\n","plt.grid(color='grey', linestyle='--', linewidth=0.5)\n","plt.xlabel('Date', fontsize=14)\n","plt.ylabel('Rolling Standard Deviation', fontsize=14)\n","plt.title('Rolling Standard Deviation \\nfor Entropy Variables of Topics Related to the U.S. Dollar', fontsize=16)\n","plt.legend(fontsize=14, frameon=False)\n","# Adjust layout to accommodate labels\n","plt.tight_layout()\n","# Specify the output path\n","output_path = '/content/drive/MyDrive/topic_plots/Robustness_of_Entropy_variables.pdf'\n","# Save the plot as a PDF\n","plt.savefig(output_path, dpi=300)\n","# Save the plot as a PDF\n","plt.savefig('plot.pdf')\n","# Show the plot\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":626},"id":"YMA21HWy8KaU","executionInfo":{"status":"ok","timestamp":1711360729527,"user_tz":-60,"elapsed":2358,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"cffc4b5b-f812-41fa-a8f9-e912c562649e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x400 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# Define your entropy variables\n","entropy_variables = ['Stock Market News', 'Economic Development News', 'FED News',\n","                     'Micro Finance News', 'International Trade News', 'EPU_US', 'EPU_UK']\n","\n","# Define colors for plotting\n","colors = [\"#6D2077\", \"#00A3A1\", \"#ff9933\", \"#BC204B\", \"#333333\"]\n","\n","# Plot mean value for each entropy variable\n","plt.figure(figsize=(10, 6))  # Adjust figure size as needed\n","\n","for i, variable in enumerate(entropy_variables):\n","    # Calculate mean value of the time series\n","    mean_value = data[variable].mean()\n","\n","    # Plot mean value as a marker with the same color\n","    plt.scatter([i], mean_value, color=colors[i % len(colors)], label=f'{variable} mean', marker='x')\n","\n","    # Add label with rounded-up value\n","    plt.text(i, mean_value, f'{mean_value:.4f}', ha='center', va='bottom')\n","\n","# Add y=0.1 line\n","plt.axhline(y=0.1, color='red', linestyle='--', label='Threshold')\n","\n","plt.xticks(np.arange(len(entropy_variables)), entropy_variables, rotation=45)  # Rotate x-axis labels by 45 degrees\n","plt.xlabel('Entropy Variables')\n","plt.ylabel('Average rolling standard deviations')\n","plt.title('Average rolling standard deviations for News Variables')\n","plt.legend()\n","plt.show()\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":706},"id":"s5_pyTCy-30P","executionInfo":{"status":"ok","timestamp":1711360730173,"user_tz":-60,"elapsed":648,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"1136a9ca-2017-4eda-9cf4-c8507e492bc1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1cAAAKxCAYAAAC/qFbbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1QU19sH8O9Sl96kiQgoFkTsXSlW7F2xREGJsWM3mtiw9xJjrLERe40xEQsBVOxiL6iIXUFRQVD6ff/gZXRdQFYXyy/fzzlzjnvnzr3PzO6OPDt37siEEAJERERERET0STS+dABERERERET/C5hcERERERERqQGTKyIiIiIiIjVgckVERERERKQGTK6IiIiIiIjUgMkVERERERGRGjC5IiIiIiIiUgMmV0RERERERGrA5IqIiIiIiEgNmFwR0X9aWFgYZDIZwsLCpDI/Pz84Ojoq1JPJZJg0adJnje1bsnbtWshkMty5c6fQ+7pz5w5kMhnWrl37Udt/jlhz+wx9qs95jD9GbGwsOnbsCAsLC8hkMixcuPBLh0TvmTRpEmQy2Udt6+joiJYtW36wXm7nVKL/EiZXRN+A3377DTKZDDVr1vzSodBX6NixY5g0aRJevnz5pUMhNZg+fTp27979pcNQ2bBhw7B//36MHTsWQUFBaNq0aaH2J5PJIJPJMG/ePKV1OYnomTNnCjWGT5Geno4iRYqgXr16edYRQsDe3h5VqlT5jJER0adgckX0DdiwYQMcHR1x6tQp3Lp160uH85/05s0bjBs37kuHkatjx44hMDCQydVXZOXKlYiKivqobfNKrnr06IE3b97AwcHhE6MrHP/++y/atGmDkSNH4rvvvkPZsmU/S79z5szB69evP0tf6qStrY1OnTrh2LFjuHv3bq51Dh8+jAcPHuC7775TS5/jxo3Dmzdv1NIWEeWOyRXRVy4mJgbHjh3D/PnzYWlpiQ0bNnz2GLKyspCSkvLZ+/0YhRWrXC6HlpaW2tul3H2Lfyy/S1tbG7q6umptU1NTE3K5/KOHdRW2uLg4mJqaqq29lJQUZGVl5VunUqVKiI2NxbJly9TW7+fUvXt3CCGwadOmXNdv3LgRGhoa6NKlyyf1k5ycDADQ0tKCXC7/pLaIKH9Mroi+chs2bICZmRlatGiBjh07KiRX6enpMDc3R69evZS2S0xMhFwux8iRI6Wy1NRUTJw4Ec7OztDV1YW9vT1Gjx6N1NRUhW1lMhkGDRqEDRs2wNXVFbq6uggODgYAzJ07F3Xq1IGFhQX09PRQtWpVbN++Xan/N2/eICAgAEWKFIGRkRFat26Nhw8f5nrv0sOHD9G7d29YW1tDV1cXrq6uWL16dYGOT36xnjt3Ds2aNYOxsTEMDQ3RsGFDnDhxokDt5tbPu3Hn3Ltw69Yt+Pn5wdTUFCYmJujVq5dSYqDKscjN4sWL4erqCn19fZiZmaFatWrYuHGjFMeoUaMAAE5OTtJQqZz7ctasWYMGDRrAysoKurq6KFeuHJYuXarUR879FEePHkWNGjUgl8tRokQJrF+/XqnulStX0KBBA+jp6aFYsWKYOnVqrn8E//nnn2jRogWKFi0KXV1dlCxZElOmTEFmZqZCPS8vL5QvXx5nz56Fh4cH9PX18dNPPwEAXr58CT8/P5iYmMDU1BS+vr4qXaEraKwAsG/fPri7u8PAwABGRkZo0aIFrly5Iq2fO3cuZDJZrlcZxo4dCx0dHbx48QJA7vdcFeS7I5PJkJycjHXr1knvpZ+fH4C877n67bffpM9+0aJFMXDgQKVjlHOMr169ivr160NfXx92dnaYPXu20r7k93nLTU5cQggsWbJEijvH7du30alTJ5ibm0NfXx+1atXC33//rdBGzn06mzdvxrhx42BnZwd9fX0kJibm2S8A1K1bFw0aNMDs2bMLdEXm+vXr6NixI8zNzSGXy1GtWjXs2bNHWv/y5Utoamril19+kcqePXsGDQ0NWFhYQAghlffv3x82NjbS65s3b6JDhw6wsbGBXC5HsWLF0KVLFyQkJOQbv6OjY67HNz09Hdu3b0f9+vVRtGhRXLx4EX5+fihRogTkcjlsbGzQu3dvxMfHK2yXc266evUqunXrBjMzM2noYW73XBX0HJHjwIEDqFSpEuRyOcqVK4edO3fmWfddJ0+eRNOmTWFiYgJ9fX14enoiIiJCoc6rV68wdOhQODo6QldXF1ZWVmjcuDEiIyML1AfR14A/wxJ95TZs2ID27dtDR0cHXbt2xdKlS3H69GlUr14d2traaNeuHXbu3Inly5dDR0dH2m737t1ITU2VfvHMyspC69atcfToUfzwww9wcXHBpUuXsGDBAty4cUNpGNK///6LrVu3YtCgQShSpIj0h+KiRYvQunVrdO/eHWlpadi8eTM6deqEvXv3okWLFtL2fn5+2Lp1K3r06IFatWohPDxcYX2O2NhY1KpVS0qSLC0tsW/fPvj7+yMxMRFDhw794DHKLdYrV67A3d0dxsbGGD16NLS1tbF8+XJ4eXkhPDxcbfevde7cGU5OTpgxYwYiIyOxatUqWFlZYdasWSofi9ysXLkSAQEB6NixI4YMGYKUlBRcvHgRJ0+eRLdu3dC+fXvcuHEDmzZtwoIFC1CkSBEAgKWlJQBg6dKlcHV1RevWraGlpYW//voLAwYMQFZWFgYOHKjQ161bt9CxY0f4+/vD19cXq1evhp+fH6pWrQpXV1cAwJMnT1C/fn1kZGRgzJgxMDAwwIoVK6Cnp6cU+9q1a2FoaIjhw4fD0NAQ//77LyZMmIDExETMmTNHoW58fDyaNWuGLl264LvvvoO1tTWEEGjTpg2OHj2Kfv36wcXFBbt27YKvr2+Bjp0qsQYFBcHX1xfe3t6YNWsWXr9+jaVLl6JevXo4d+4cHB0d0blzZ4wePRpbt26VEtocW7duRZMmTWBmZpZnPAX57gQFBeH7779HjRo18MMPPwAASpYsmWebkyZNQmBgIBo1aoT+/fsjKipKOkdERERAW1tbqvvixQs0bdoU7du3R+fOnbF9+3b8+OOPcHNzQ7NmzQB8+POWGw8PDwQFBaFHjx5o3LgxevbsKa2LjY1FnTp18Pr1awQEBMDCwgLr1q1D69atsX37drRr106hrSlTpkBHRwcjR45Eamqqwjktv2Pg4eGBpUuXYvjw4XnWu3LlCurWrQs7Ozvp87B161a0bdsWO3bsQLt27WBqaory5cvj8OHDCAgIAAAcPXoUMpkMz58/x9WrV6XvwpEjR+Du7g4ASEtLg7e3N1JTUzF48GDY2Njg4cOH2Lt3L16+fAkTE5NcY5LJZOjWrRumT5+OK1euSG0DQHBwMJ4/f47u3bsDAA4ePIjbt2+jV69esLGxwZUrV7BixQpcuXIFJ06cUEqaOnXqhFKlSmH69OkKSeH7VDlH3Lx5Ez4+PujXrx98fX2xZs0adOrUCcHBwWjcuHGeffz7779o1qwZqlatiokTJ0JDQ0NK6o4cOYIaNWoAAPr164ft27dj0KBBKFeuHOLj43H06FFcu3aN953Rt0MQ0VfrzJkzAoA4ePCgEEKIrKwsUaxYMTFkyBCpzv79+wUA8ddffyls27x5c1GiRAnpdVBQkNDQ0BBHjhxRqLds2TIBQEREREhlAISGhoa4cuWKUkyvX79WeJ2WlibKly8vGjRoIJWdPXtWABBDhw5VqOvn5ycAiIkTJ0pl/v7+wtbWVjx79kyhbpcuXYSJiYlSf+/LK9a2bdsKHR0dER0dLZU9evRIGBkZCQ8PD6ksNDRUABChoaFSma+vr3BwcFDq5924J06cKACI3r17K9Rr166dsLCw+KhjkZs2bdoIV1fXfOvMmTNHABAxMTFK63I7ft7e3gqfDSGEcHBwEADE4cOHpbK4uDihq6srRowYIZUNHTpUABAnT55UqGdiYqIUQ2599+3bV+jr64uUlBSpzNPTUwAQy5YtU6i7e/duAUDMnj1bKsvIyBDu7u4CgFizZo3ywXhHQWN99eqVMDU1FX369FHY/smTJ8LExEShvHbt2qJq1aoK9U6dOiUAiPXr10tluX2GCvLdEUIIAwMD4evrq7Q/a9asUYg7Li5O6OjoiCZNmojMzEyp3q+//ioAiNWrV0tlOcf43RhTU1OFjY2N6NChg1RWkM9bXgCIgQMHKpTlvAfvnndevXolnJychKOjoxR3zvewRIkSH/zO59Zf/fr1hY2NjbRtzrE6ffq0VL9hw4bCzc1N4bOXlZUl6tSpI0qVKiWVDRw4UFhbW0uvhw8fLjw8PISVlZVYunSpEEKI+Ph4IZPJxKJFi4QQQpw7d04AENu2bStQ7O+6cuWKACDGjh2rUN6lSxchl8tFQkKCECL379OmTZuUvrc556auXbsq1c9Z9y5VzxE7duyQyhISEoStra2oXLmyVPb+OTUrK0uUKlVKeHt7i6ysLIV+nZycROPGjaUyExMTpc8Q0beGwwKJvmIbNmyAtbU16tevDyD7V04fHx9s3rxZGlrVoEEDFClSBFu2bJG2e/HiBQ4ePAgfHx+pbNu2bXBxcUHZsmXx7NkzaWnQoAEAIDQ0VKFvT09PlCtXTimmd3/1f/HiBRISEuDu7q4wbCNnWN6AAQMUth08eLDCayEEduzYgVatWkEIoRCXt7c3EhISCjQc5P1YMzMzceDAAbRt2xYlSpSQym1tbdGtWzccPXr0g8ONCqpfv34Kr93d3REfHy+1X9BjkRdTU1M8ePAAp0+f/qj43n2/EhIS8OzZM3h6euL27dtKw5XKlSsn/RIPZF/9KlOmDG7fvi2V/fPPP6hVq5b0S3NOvZxf1/Pq+9WrV3j27Bnc3d3x+vVrXL9+XaGurq6u0vDWf/75B1paWujfv79UpqmpWeBjV9BYDx48iJcvX6Jr164Kn0FNTU3UrFlT4bvh4+ODs2fPIjo6WirbsmULdHV10aZNm3zjKch3RxWHDh1CWloahg4dCg2Nt/+d9+nTB8bGxkpD7wwNDRUmRtDR0UGNGjUU3t9P/by9759//kGNGjUUZsQzNDTEDz/8gDt37uDq1asK9X19fXO9svghkyZNwpMnT/K89+r58+f4999/0blzZ+mz+OzZM8THx8Pb2xs3b97Ew4cPAWR/h2NjY6UJSY4cOQIPDw+4u7vjyJEjALKvZgkhpO9LzpWp/fv3q3y/YLly5VC5cmVs3rxZKktOTsaePXvQsmVLGBsbA1D8/KSkpODZs2eoVasWAOT6GXr/3JQXVc4RRYsWVbjaaGxsjJ49e+LcuXN48uRJru2fP38eN2/eRLdu3RAfHy8d++TkZDRs2BCHDx+Whuqampri5MmTePToUYFiJ/oaMbki+kplZmZi8+bNqF+/PmJiYnDr1i3cunULNWvWRGxsLEJCQgBk36DcoUMH/Pnnn9K9Uzt37kR6erpCcnXz5k1cuXIFlpaWCkvp0qUBZN+M/i4nJ6dc49q7dy9q1aoFuVwOc3NzWFpaYunSpQr/Cd+9excaGhpKbTg7Oyu8fvr0KV6+fIkVK1YoxZXzh/b7ceXm/X6ePn2K169fo0yZMkp1XVxckJWVhfv373+w3YIoXry4wuucYWE5994U9Fjk5ccff4ShoSFq1KiBUqVKYeDAgUr3KeQnIiICjRo1goGBAUxNTWFpaSndz/T+H07v70vO/uTsS87+lCpVSqlebsf6ypUraNeuHUxMTGBsbAxLS0vpj/v3+7azs1MaAnb37l3Y2trC0NDwg33lpqCx3rx5E0D2DxXvfw4PHDig8Bns1KkTNDQ0pB8zhBDYtm2bdG9ffgry3VFFzr1f7++Pjo4OSpQooXRvWLFixZSGjr3//n7q5y23GPP6Hr67DznyOu98iIeHB+rXr5/nvVe3bt2CEALjx49Xeo8nTpwI4O25JidhOnLkCJKTk3Hu3Dm4u7vDw8NDSq6OHDkCY2NjVKxYUYp7+PDhWLVqFYoUKQJvb28sWbKkwO9t9+7dpcmLgOxh3a9fv1b4IeD58+cYMmQIrK2toaenB0tLS+l45dZPQY+lKucIZ2dnpc9Qzv8heT1/Lef75evrq3TsV61ahdTUVKmf2bNn4/Lly7C3t0eNGjUwadIkheSf6FvAe66IvlL//vsvHj9+jM2bNyv8opljw4YNaNKkCQCgS5cuWL58Ofbt24e2bdti69atKFu2rPQfP5B9z5Wbmxvmz5+fa3/29vYKr3P79fjIkSNo3bo1PDw88Ntvv8HW1hba2tpYs2ZNvje85yXn18rvvvsuz/toKlSo8MF2PuaXbnXR1NTMtVzkc4+DKlxcXBAVFYW9e/ciODgYO3bswG+//YYJEyYgMDAw322jo6PRsGFDlC1bFvPnz4e9vT10dHTwzz//YMGCBUoTO6hzX16+fAlPT08YGxtj8uTJKFmyJORyOSIjI/Hjjz8q9f0l38OcWIKCghQmKMjx7iyRRYsWhbu7O7Zu3YqffvoJJ06cwL179xTuscuNur87H6Mg7++nfN7U4VM+BxMnToSXlxeWL1+uNGthzns8cuRIeHt757p9zg8eRYsWhZOTEw4fPgxHR0cIIVC7dm1YWlpiyJAhuHv3Lo4cOYI6deooXDGcN28e/Pz88Oeff+LAgQMICAjAjBkzcOLECRQrVizf2Lt27YrRo0dj48aNqFOnDjZu3AgzMzM0b95cqtO5c2ccO3YMo0aNQqVKlWBoaIisrCw0bdo010laCnIsVT1HfIycNubMmYNKlSrlWifnB5TOnTvD3d0du3btwoEDBzBnzhzMmjULO3fulO4LJPraMbki+kpt2LABVlZWWLJkidK6nTt3YteuXVi2bBn09PTg4eEBW1tbbNmyBfXq1cO///6Ln3/+WWGbkiVL4sKFC2jYsOFHT+W8Y8cOyOVy7N+/X2Ga6TVr1ijUc3BwQFZWFmJiYhSuHLz/jC5LS0sYGRkhMzMTjRo1+qiYcmNpaQl9ff1cnzN0/fp1aGhoKCWThaWgxyI/BgYG8PHxgY+PD9LS0tC+fXtMmzYNY8eOzXdq7r/++gupqanYs2ePwlWp94eAqro/Ob9Ev+v9Yx0WFob4+Hjs3LkTHh4eUnlMTIxKfYWEhCApKUnh6lVBnx9V0FhzJoywsrIq0OfQx8cHAwYMQFRUFLZs2QJ9fX20atUq320K+t0BUODvZ87zrqKiohSGv6alpSEmJuajv1Mf+rypwsHBIc/v4bv7oA6enp7w8vLCrFmzMGHCBIV1OcdHW1u7QMfF3d0dhw8fhpOTEypVqgQjIyNUrFgRJiYmCA4ORmRkZK7JppubG9zc3DBu3DgcO3YMdevWxbJlyzB16tR8+ytatCjq16+Pbdu2Yfz48Th48CD8/Pykq7kvXrxASEgIAgMDFfYtt8+3KlQ9R+RcAXz3M3rjxg0AUJodM0fO98vY2LhAx97W1hYDBgzAgAEDEBcXhypVqmDatGlMruibwWGBRF+hN2/eYOfOnWjZsiU6duyotAwaNAivXr2SphDW0NBAx44d8ddffyEoKAgZGRkKQwKB7F8EHz58iJUrV+baX85zUPKjqakJmUymMJX2nTt3lGYazPll+LffflMoX7x4sVJ7HTp0wI4dO3D58mWl/p4+ffrBmPKKs0mTJvjzzz8VhqrExsZi48aNqFev3geHcKlLQY9FXt6fZllHRwflypWDEALp6ekAsv8YBqA0/XbOlYp3r0wkJCTk+gd9QTVv3hwnTpzAqVOnpLKnT58qPX8tt77T0tKUjsOH+srIyFCYFjozM7PAx66gsXp7e8PY2BjTp0+Xjum73v8cdujQAZqamti0aRO2bduGli1bSu9BXgr63QGy38+CTDffqFEj6Ojo4JdfflE4zr///jsSEhIKPCPluwryeVNF8+bNcerUKRw/flwqS05OxooVK+Do6JjrfZ2fIufeqxUrViiUW1lZSVe1Hj9+rLTd+++xu7s77ty5gy1btkjDBDU0NFCnTh3Mnz8f6enpCvcnJiYmIiMjQ6ENNzc3aGhoKD3qIi/du3dHXFwc+vbti/T0dIUhgbl9nwBg4cKFBWo7L6qeIx49eoRdu3ZJrxMTE7F+/XpUqlQp16u+AFC1alWULFkSc+fORVJSktL6nGOfmZmpNAzRysoKRYsWLfAxJPoa8MoV0Vdoz549ePXqFVq3bp3r+lq1akkPFM5Jonx8fLB48WJMnDgRbm5u0j0NOXr06IGtW7eiX79+CA0NRd26dZGZmYnr169j69at2L9/P6pVq5ZvXC1atMD8+fPRtGlTdOvWDXFxcViyZAmcnZ1x8eJFqV7VqlXRoUMHLFy4EPHx8dL04zm/cL77q+fMmTMRGhqKmjVrok+fPihXrhyeP3+OyMhIHDp0CM+fP/+oYzh16lQcPHgQ9erVw4ABA6ClpYXly5cjNTU112f7FBZVjkVumjRpAhsbG9StWxfW1ta4du0afv31V7Ro0QJGRkZSHwDw888/o0uXLtDW1karVq3QpEkT6OjooFWrVujbty+SkpKwcuVKWFlZ5foHZkGMHj0aQUFBaNq0KYYMGSJNb+7g4KDwGahTpw7MzMzg6+uLgIAAyGQyBAUFqTTEsFWrVqhbty7GjBmDO3fuSM/UKeh9LAWN1djYGEuXLkWPHj1QpUoVdOnSBZaWlrh37x7+/vtv1K1bF7/++qtU38rKCvXr18f8+fPx6tUrpR8yclPQ7w6Q/X4eOnQI8+fPl4ao5fboAEtLS4wdOxaBgYFo2rQpWrdujaioKPz222+oXr26wuQVBVWQz5sqxowZg02bNqFZs2YICAiAubk51q1bh5iYGOzYsUNhWJ06eHp6wtPTE+Hh4UrrlixZgnr16sHNzQ19+vRBiRIlEBsbi+PHj+PBgwe4cOGCVDcncYqKisL06dOlcg8PD+zbtw+6urqoXr26VP7vv/9i0KBB6NSpE0qXLo2MjAwEBQVJPyAVRIcOHTBgwAD8+eefsLe3V7jia2xsDA8PD8yePRvp6emws7PDgQMHVLoSnBtVzxGlS5eGv78/Tp8+DWtra6xevRqxsbH5/mCjoaGBVatWoVmzZnB1dUWvXr1gZ2eHhw8fIjQ0FMbGxvjrr7/w6tUrFCtWDB07dkTFihVhaGiIQ4cO4fTp05g3b94n7SfRZ/W5pyckog9r1aqVkMvlIjk5Oc86fn5+QltbW5rCPCsrS9jb2wsAYurUqbluk5aWJmbNmiVcXV2Frq6uMDMzE1WrVhWBgYHSdL9C5D6lco7ff/9dlCpVSujq6oqyZcuKNWvW5Dq9b3Jyshg4cKAwNzcXhoaGom3btiIqKkoAEDNnzlSoGxsbKwYOHCjs7e2Ftra2sLGxEQ0bNhQrVqz44LHKL9bIyEjh7e0tDA0Nhb6+vqhfv744duyYQp1PnYr96dOnCvXeny5b1WPxvuXLlwsPDw9hYWEhdHV1RcmSJcWoUaMU3i8hhJgyZYqws7MTGhoaCv3v2bNHVKhQQcjlcuHo6ChmzZolVq9erRSjg4ODaNGihVL/np6ewtPTU6Hs4sWLwtPTU8jlcmFnZyemTJkifv/9d6U2IyIiRK1atYSenp4oWrSoGD16tPTogHePt6enZ57Tf8fHx4sePXoIY2NjYWJiInr06CFNe/2hqdhViVWI7M+Ct7e3MDExEXK5XJQsWVL4+fmJM2fOKLW7cuVKAUAYGRmJN2/eKK3P7TNU0O/O9evXhYeHh9DT0xMApGnZc/tsCZE99XrZsmWFtra2sLa2Fv379xcvXrxQqJPXMX4/zoJ+3nKT13cxOjpadOzYUZiamgq5XC5q1Kgh9u7dq1An53uoylTmefWX0xbem4o9J5aePXsKGxsboa2tLezs7ETLli3F9u3bldqxsrISAERsbKxUdvToUQFAuLu7K9S9ffu26N27tyhZsqSQy+XC3Nxc1K9fXxw6dKjA+yOEEJ06dRIAxOjRo5XWPXjwQLRr106YmpoKExMT0alTJ/Ho0aMCn5veXfcuVc8R+/fvFxUqVJA+x++/Z7mdU4XInq6+ffv20mfLwcFBdO7cWYSEhAghsh8NMGrUKFGxYkVhZGQkDAwMRMWKFcVvv/2m0jEk+tJkQqjprmsiog84f/48KleujD/++CPXqbv/S3gsiIiI/vfwnisiKhS5TYe8cOFCaGhoKAx3+S/gsSAiIvpv4D1XRFQoZs+ejbNnz6J+/frQ0tLCvn37sG/fPvzwww+fbaa+rwWPBRER0X8DhwUSUaE4ePAgAgMDcfXqVSQlJaF48eLo0aMHfv75Z4XnBv0X8FgQERH9NzC5IiIiIiIiUgPec0VERERERKQGTK6IiIiIiIjUgIP9c5GVlYVHjx7ByMjogw/4JCIiIiKi/11CCLx69QpFixb94MPPmVzl4tGjR5zBi4iIiIiIJPfv30exYsXyrcPkKhdGRkYAsg+gsbHxF46GiIiIiIi+lMTERNjb20s5Qn6YXOUiZyigsbExkysiIiIiIirQ7UKc0IKIiIiIiEgNmFwRERERERGpAZMrIiIiIiIiNWByRUREREREpAZMroiIiIiIiNSAyRUREREREZEaMLkiIiIiIiJSAyZXREREREREasDkioiIiIiISA2YXBEREREREakBkysiIiIiIiI1YHJFRERERESkBkyuiIiIiIiI1IDJFRERERERkRp88eRqyZIlcHR0hFwuR82aNXHq1Kk86165cgUdOnSAo6MjZDIZFi5c+MltEhERERERqcMXTa62bNmC4cOHY+LEiYiMjETFihXh7e2NuLi4XOu/fv0aJUqUwMyZM2FjY6OWNomIiIiIiNRBJoQQX6rzmjVronr16vj1118BAFlZWbC3t8fgwYMxZsyYfLd1dHTE0KFDMXToULW1mSMxMREmJiZIePQIxsbGyhU0NQG5/O3r5OS8G9PQAPT0Pq7u69dAXm+PTAbo639c3TdvgKysvOMwMPi4uikpQGameurq62fHDQCpqUBGhnrq6ullH2cASEsD0tPVU1cuz/5cqFo3PT27fl50dQEtLdXrZmRkH4u86OgA2tqq183MzH7v8qKtnV1f1bpZWdmfNXXU1dLKPhZA9nfi9Wv11FXle89zRO51eY5QvS7PEdn/5jni4+ryHJH9b54jVK/Lc0T2v///e5+YmAiTokWRkJCQe27wLvGFpKamCk1NTbFr1y6F8p49e4rWrVt/cHsHBwexYMECtbSZkpIiEhISpOX+/fsCgEjIPqTKS/Pmig3o6+deDxDC01OxbpEiedetVu39ncy7brlyinXLlcu7roODYt1q1fKuW6SIYl1Pz7zr6usr1m3ePO+673/UOnbMv25S0tu6vr75142Le1t3wID868bEvK07cmT+dS9fflt34sT865469bbu7Nn51w0NfVv311/zr7t379u6a9bkX3fr1rd1t27Nv+6aNW/r7t2bf91ff31bNzQ0/7qzZ7+te+pU/nUnTnxb9/Ll/OuOHPm2bkxM/nUHDHhbNy4u/7q+vm/rJiXlX7djR6Egv7o8R2QvPEe8XXiOyF54jsheeI7IXniOeLvwHJG9fKXniARAABAJCQniQ77YsMBnz54hMzMT1tbWCuXW1tZ48uTJZ21zxowZMDExkRZ7e/uP6p+IiIiIiP67vtiwwEePHsHOzg7Hjh1D7dq1pfLRo0cjPDwcJ0+ezHf73IYFfmybqampSH3ncmZiYiLs7e05LFDVurycr3pdXs7P/jeH/HxcXZ4jsv/Nc4TqdXmOyP43zxEfV5fniOx/8xyhet1v9ByhyrBArXzXFqIiRYpAU1MTsbGxCuWxsbF5TlZRWG3q6upCN+cgvsvAQPGLnJeC1PmYuu+eyNRZ990TrzrrvvsfhTrr6uq+/ZCrs66Oztsv2peqq6399oSjzrpaWm9PkOqsq6lZ8M+wKnU1NAqnrkxWOHWBr6MuzxHZeI5QvS7PEdl4jvi4ujxHZOM5QvW63+o5Ir9E/v3mC1xTzXR0dFC1alWEhIRIZVlZWQgJCVG46vSl2yQiIiIiIiqIL3blCgCGDx8OX19fVKtWDTVq1MDChQuRnJyMXr16AQB69uwJOzs7zJgxAwCQlpaGq1evSv9++PAhzp8/D0NDQzg7OxeoTSIiIiIiosLwRZMrHx8fPH36FBMmTMCTJ09QqVIlBAcHSxNS3Lt3Dxoaby+uPXr0CJUrV5Zez507F3PnzoWnpyfCwsIK1CYREREREVFh+KLPufpaSc+5Kshc9kRERERE9D9Lldzgi91zRURERERE9L+EyRUREREREZEaMLkiIiIiIiJSAyZXREREREREasDkioiIiIiISA2YXBEREREREakBkysiIiIiIiI1YHJFRERERESkBkyuiIiIiIiI1IDJFRERERERkRowuSIiIiIiIlIDJldERERERERqwOSKiIiIiIhIDZhcERERERERqQGTKyIiIiIiIjVgckVERERERKQGTK6IiIiIiIjUgMkVERERERGRGjC5IiIiIiIiUgMmV0RERERERGrA5IqIiIiIiEgNmFwRERERERGpAZMrIiIiIiIiNWByRUREREREpAZMroiIiIiIiNSAyRUREREREZEaMLkiIiIiIiJSAyZXREREREREasDkioiIiIiISA2YXBEREREREakBkysiIiIiIiI1YHJFRERERESkBkyuiIiIiIiI1IDJFRERERERkRowuSIiIiIiIlIDJldERERERERqwOSKiIiIiIhIDZhcERERERERqQGTKyIiIiIiIjVgckVERERERKQGTK6IiIiIiIjUgMkVERERERGRGjC5IiIiIiIiUgMmV0RERERERGrA5IqIiIiIiEgNmFwRERERERGpAZMrIiIiIiIiNWByRUREREREpAZMroiIiIiIiNSAyRUREREREZEaMLkiIiIiIiJSAyZXREREREREasDkioiIiIiISA2YXBEREREREakBkysiIiIiIiI1YHJFRERERESkBkyuiIiIiIiI1IDJFRERERERkRowuSIiIiIiIlIDJldERERERERqwOSKiIiIiIhIDZhcERERERERqQGTKyIiIiIiIjVgckVERERERKQGTK6IiIiIiIjUgMkVERERERGRGjC5IiIiIiIiUgMmV0RERERERGrA5IqIiIiIiEgNmFwRERERERGpAZMrIiIiIiIiNWByRUREREREpAZfPLlasmQJHB0dIZfLUbNmTZw6dSrf+tu2bUPZsmUhl8vh5uaGf/75R2F9UlISBg0ahGLFikFPTw/lypXDsmXLCnMXiIiIiIiIvmxytWXLFgwfPhwTJ05EZGQkKlasCG9vb8TFxeVa/9ixY+jatSv8/f1x7tw5tG3bFm3btsXly5elOsOHD0dwcDD++OMPXLt2DUOHDsWgQYOwZ8+ez7VbRERERET0HyQTQogv1XnNmjVRvXp1/PrrrwCArKws2NvbY/DgwRgzZoxSfR8fHyQnJ2Pv3r1SWa1atVCpUiXp6lT58uXh4+OD8ePHS3WqVq2KZs2aYerUqQWKKzExESYmJkhISICxsfGn7CIREREREX3DVMkNvtiVq7S0NJw9exaNGjV6G4yGBho1aoTjx4/nus3x48cV6gOAt7e3Qv06depgz549ePjwIYQQCA0NxY0bN9CkSZM8Y0lNTUViYqLCQkREREREpIovllw9e/YMmZmZsLa2Vii3trbGkydPct3myZMnH6y/ePFilCtXDsWKFYOOjg6aNm2KJUuWwMPDI89YZsyYARMTE2mxt7f/hD0jIiIiIqL/oi8+oYW6LV68GCdOnMCePXtw9uxZzJs3DwMHDsShQ4fy3Gbs2LFISEiQlvv373/GiImIiIiI6H+B1pfquEiRItDU1ERsbKxCeWxsLGxsbHLdxsbGJt/6b968wU8//YRdu3ahRYsWAIAKFSrg/PnzmDt3rtKQwhy6urrQ1dX91F0iIiIiIqL/sC925UpHRwdVq1ZFSEiIVJaVlYWQkBDUrl07121q166tUB8ADh48KNVPT09Heno6NDQUd0tTUxNZWVlq3gMiIiIiIqK3vtiVKyB72nRfX19Uq1YNNWrUwMKFC5GcnIxevXoBAHr27Ak7OzvMmDEDADBkyBB4enpi3rx5aNGiBTZv3owzZ85gxYoVAABjY2N4enpi1KhR0NPTg4ODA8LDw7F+/XrMnz//i+0nERERERH971M5uYqMjIS2tjbc3NwAAH/++SfWrFmDcuXKYdKkSdDR0SlwWz4+Pnj69CkmTJiAJ0+eoFKlSggODpYmrbh3757CVag6depg48aNGDduHH766SeUKlUKu3fvRvny5aU6mzdvxtixY9G9e3c8f/4cDg4OmDZtGvr166fqrhIRERERERWYys+5ql69OsaMGYMOHTrg9u3bcHV1Rbt27XD69Gm0aNECCxcuLKRQPx8+54qIiIiIiIBCfs7VjRs3UKlSJQDAtm3b4OHhgY0bN2Lt2rXYsWPHRwVMRERERET0rVM5uRJCSJNDHDp0CM2bNwcA2Nvb49mzZ+qNjoiIiIiI6BuhcnJVrVo1TJ06FUFBQQgPD5emPI+JiVF6wC8REREREdF/hcrJ1cKFCxEZGYlBgwbh559/hrOzMwBg+/btqFOnjtoDJCIiIiIi+haoPKFFXlJSUqCpqQltbW11NPdFcUILIiIiIiICVMsNPvo5V2lpaYiLi1N6OG/x4sU/tkkiIiIiIqJvlsrJ1Y0bN+Dv749jx44plAshIJPJkJmZqbbgiIiIiIiIvhUqJ1e9evWClpYW9u7dC1tbW8hkssKIi4iIiIiI6JuicnJ1/vx5nD17FmXLli2MeIiIiIiIiL5JKs8WWK5cOT7PioiIiIiI6D0qJ1ezZs3C6NGjERYWhvj4eCQmJiosRERERERE/0UqT8WuoZGdj71/r9X/0oQWnIqdiIiIiIiAQp6KPTQ09KMDIyIiIiIi+l+lcnLl6elZGHEQERERERF90z7qIcIvX77E77//jmvXrgEAXF1d0bt3b5iYmKg1OCIiIiIiom+FyhNanDlzBiVLlsSCBQvw/PlzPH/+HPPnz0fJkiURGRlZGDESERERERF99VSe0MLd3R3Ozs5YuXIltLSyL3xlZGTg+++/x+3bt3H48OFCCfRz4oQWREREREQEqJYbqJxc6enp4dy5c0oPEb569SqqVauG169fqx7xV4bJFRERERERAarlBioPCzQ2Nsa9e/eUyu/fvw8jIyNVmyMiIiIiIvqfoHJy5ePjA39/f2zZsgX379/H/fv3sXnzZnz//ffo2rVrYcRIRERERET01VN5tsC5c+dCJpOhZ8+eyMjIAABoa2ujf//+mDlzptoDJCIiIiIi+haofM9VjtevXyM6OhoAULJkSejr66s1sC+J91wRERERERGgWm7wUc+5AgB9fX24ubl97OZERERERET/UwqUXLVv3x5r166FsbEx2rdvn2/dnTt3qiUwIiIiIiKib0mBkisTExPIZDIA2bMF5vybiIiIiIiIsn30PVf/y3jPFRERERERAYX8nKsGDRrg5cuXuXbaoEEDVZsjIiIiIiL6n6BychUWFoa0tDSl8pSUFBw5ckQtQREREREREX1rCjxb4MWLF6V/X716FU+ePJFeZ2ZmIjg4GHZ2duqNjoiIiIiI6BtR4OSqUqVKkMlkkMlkuQ7/09PTw+LFi9UaHBERERER0beiwMlVTEwMhBAoUaIETp06BUtLS2mdjo4OrKysoKmpWShBEhERERERfe0KnFw5ODgAALKysgotGCIiIiIiom9VgZOr9129ehX37t1TmtyidevWnxwUERERERHRt0bl5Or27dto164dLl26BJlMhpzHZOU8WDgzM1O9ERIREdE3LTMzE+np6V86DCKiXGlqakJLS0vKZz6FysnVkCFD4OTkhJCQEDg5OeHUqVOIj4/HiBEjMHfu3E8OiIiIiP53JCUl4cGDB9KPsUREXyN9fX3Y2tpCR0fnk9pRObk6fvw4/v33XxQpUgQaGhrQ0NBAvXr1MGPGDAQEBODcuXOfFBARERH9b8jMzMSDBw+gr68PS0tLtfwqTESkTkIIpKWl4enTp4iJiUGpUqWgoaHyo4AlKidXmZmZMDIyAgAUKVIEjx49QpkyZeDg4ICoqKiPDoSIiIj+t6Snp0MIAUtLS+jp6X3pcIiIcqWnpwdtbW3cvXsXaWlpkMvlH92WyslV+fLlceHCBTg5OaFmzZqYPXs2dHR0sGLFCpQoUeKjAyEiIqL/TbxiRURfu0+5WvUulZOrcePGITk5GQAwefJktGzZEu7u7rCwsMCWLVvUEhQREREREdG3RuXkytvbW/q3s7Mzrl+/jufPn8PMzIy/TBERERER0X+WWq5/mZubM7EiIiIi+gRr166Fqanplw4jV5MmTUKlSpW+dBhEX70CJVft27dHYmKi9O/8FiIiIqJv2dOnT9G/f38UL14curq6sLGxgbe3NyIiIqQ6MpkMu3fv/nJB/j8vLy/IZDLMnDlTaV2LFi0gk8kwadKkzx/YewqaOK5duxYymQxNmzZVKH/58iVkMhnCwsIKJ0AiNSnQsEATExPpypSJiUmhBkREREQEAG8SU5CSnAozW+W/PV48ToDcQBd6xh8/q1deOnTogLS0NKxbtw4lSpRAbGwsQkJCEB8fr/a+1MHe3h5r167FmDFjpLKHDx8iJCQEtra2n9S2EAKZmZmfGqJKtLS0cOjQIYSGhqJ+/fqftW+iT1WgK1dr1qyRpl9fs2ZNvgsRERHRp3qTmIKlfqvxS5flePHopcK6F49e4pcuy7HUbzXeJKaotd+XL1/iyJEjmDVrFurXrw8HBwfUqFEDY8eORevWrQEAjo6OAIB27dpBJpNJrwFg6dKlKFmyJHR0dFCmTBkEBQUptd+3b19YW1tDLpejfPny2Lt3b66xPH36FNWqVUO7du2QmpqaZ8wtW7bEs2fPFK6srVu3Dk2aNIGVlZVC3aCgIFSrVg1GRkawsbFBt27dEBcXJ60PCwuDTCbDvn37ULVqVejq6uLo0aNKfUZHR6NEiRIYNGgQhBBITU3FyJEjYWdnBwMDA9SsWVO6yhQWFoZevXohISEBMpnsg1fTDAwM0Lt3b4VkMTf3799H586dYWpqCnNzc7Rp0wZ37twBAFy+fBkaGhp4+vQpAOD58+fQ0NBAly5dpO2nTp2KevXqAQBevHiB7t27S48NKFWqVL5/13p5eWHw4MEYOnQozMzMYG1tjZUrVyI5ORm9evWCkZERnJ2dsW/fPoXtLl++jGbNmsHQ0BDW1tbo0aMHnj17Jq0PDg5GvXr1YGpqCgsLC7Rs2RLR0dHS+jt37kAmk2Hnzp2oX78+9PX1UbFiRRw/fjzfY0Wfj8r3XE2dOhUxMTGFEQsRERERACAlORWv4pMQf+85fum6QkqwXjx6iV+6rkD8ved4FZ+ElOS8k46PYWhoCENDQ+zevTvPhOb06dMAsn9wfvz4sfR6165dGDJkCEaMGIHLly+jb9++6NWrF0JDQwEAWVlZaNasGSIiIvDHH3/g6tWrmDlzJjQ1NZX6uH//Ptzd3VG+fHls374durq6ecaso6OD7t27KyQDa9euRe/evZXqpqenY8qUKbhw4QJ2796NO3fuwM/PT6nemDFjMHPmTFy7dg0VKlRQWHfx4kXUq1cP3bp1w6+//gqZTIZBgwbh+PHj2Lx5My5evIhOnTqhadOmuHnzJurUqYOFCxfC2NgYjx8/xuPHjzFy5Mg89wfIvsfr0qVL2L59e67r09PT4e3tDSMjIxw5cgQREREwNDRE06ZNkZaWBldXV1hYWCA8PBwAcOTIEYXXABAeHg4vLy8AwPjx43H16lXs27cP165dw9KlS1GkSJF8Y1y3bh2KFCmCU6dOYfDgwejfvz86deqEOnXqIDIyEk2aNEGPHj3w+vVrANmJdYMGDVC5cmWcOXMGwcHBiI2NRefOnaU2k5OTMXz4cJw5cwYhISHQ0NBAu3btkJWVpdD3zz//jJEjR+L8+fMoXbo0unbtioyMjHzjpc9EqKhChQpCQ0ND1K5dWyxZskQ8ffpU1Sa+egkJCQKASEhI+NKhEBERfbPevHkjrl69Kt68efNR2z9/+EJM8pglBjv+KCZ5zBLRZ+4ovH7+8IV6A/5/27dvF2ZmZkIul4s6deqIsWPHigsXLijUASB27dqlUFanTh3Rp08fhbJOnTqJ5s2bCyGE2L9/v9DQ0BBRUVG59rtmzRphYmIirl+/Luzt7UVAQIDIysrKN1ZPT08xZMgQcf78eWFkZCSSkpJEeHi4sLKyEunp6aJixYpi4sSJeW5/+vRpAUC8evVKCCFEaGioACB2796tUG/ixImiYsWKIiIiQpiZmYm5c+dK6+7evSs0NTXFw4cPFbZp2LChGDt2rMK+fci79caMGSNKly4t0tPTxYsXLwQAERoaKoQQIigoSJQpU0bh+KSmpgo9PT2xf/9+IYQQ7du3FwMHDhRCCDF06FAxatQoYWZmJq5duybS0tKEvr6+OHDggBBCiFatWolevXp9ML4cnp6eol69etLrjIwMYWBgIHr06CGVPX78WAAQx48fF0IIMWXKFNGkSROFdu7fvy8A5PmZePr0qQAgLl26JIQQIiYmRgAQq1atkupcuXJFABDXrl0rcPykLL/zlSq5gcpXri5cuICLFy/Cy8sLc+fORdGiRdGiRQts3LhRysyJiIiIPpVZUVMEbPoBFsXNEX/vORZ2XIr4e89hUdwcAZt+gFlR00Lpt0OHDnj06BH27NmDpk2bIiwsDFWqVMHatWvz3e7atWuoW7euQlndunVx7do1AMD58+dRrFgxlC5dOs823rx5A3d3d7Rv3x6LFi0q8GzMFStWRKlSpbB9+3asXr0aPXr0gJaW8q31Z8+eRatWrVC8eHEYGRnB09MTAHDv3j2FetWqVVPa9t69e2jcuDEmTJiAESNGSOWXLl1CZmYmSpcuLV35MzQ0RHh4uMKQNlX9+OOPePr0KVavXq207sKFC7h16xaMjIyk/szNzZGSkiL16enpKQ1NDA8PR4MGDeDh4YGwsDCcPn0a6enp0vvVv39/bN68GZUqVcLo0aNx7NixD8b37hU9TU1NWFhYwM3NTSqztrYGAGnY5YULFxAaGqpwjMqWLQsAUsw3b95E165dUaJECRgbG0tDTt9/f97tO+e+uneHd9KX81FTsbu6umL69Om4ffs2QkND4ejoiKFDh8LGxkbd8REREdF/mFlRU/SY76NQ1mO+T6ElVjnkcjkaN26M8ePH49ixY/Dz88PEiRM/qU09Pb0P1tHV1UWjRo2wd+9ePHz4UKX2e/fujSVLlmD79u25DglMTk6Gt7c3jI2NsWHDBpw+fRq7du0CAKSlpSnUNTAwUNre0tISNWrUwKZNm6RZpAEgKSkJmpqaOHv2LM6fPy8t165dw6JFi1Tah3eZmppi7NixCAwMVPoBPykpCVWrVlXo7/z587hx4wa6desGIPu+qKtXr+LmzZu4evUq6tWrBy8vL4SFhSE8PBzVqlWDvr4+AKBZs2a4e/cuhg0bhkePHqFhw4YfHLqora2t8FomkymU5STGOUP6kpKS0KpVK6WYb968CQ8PDwBAq1at8Pz5c6xcuRInT57EyZMnASi/P/n1Q1/WJz/nysDAAHp6etDR0UF6ero6YiIiIiICkH2PVdDwLQplQcO3KE1yUdjKlSuH5ORk6bW2trbSLHouLi4Kk0oAQEREBMqVKwcg+2rDgwcPcOPGjTz70dDQQFBQEKpWrYr69evj0aNHBY6xW7duuHTpEsqXLy/1+a7r168jPj4eM2fOhLu7O8qWLavS1Q49PT3s3bsXcrkc3t7eePXqFQCgcuXKyMzMRFxcHJydnRWWnB/edXR0PmrWwcGDB0NDQ0MpSatSpQpu3rwJKysrpT5zZrZ2c3ODmZkZpk6dikqVKsHQ0BBeXl4IDw9HWFiYdL9VDktLS/j6+uKPP/7AwoULsWLFCpXjzU+VKlVw5coVODo6KsVsYGCA+Ph4REVFYdy4cWjYsCFcXFzw4sULtcZAhe+jkquYmBhMmzYNrq6uqFatGs6dO4fAwEA8efJE3fERERHRf9S7k1dYFDfH0O39pSGC705yoU7x8fFo0KAB/vjjD1y8eBExMTHYtm0bZs+ejTZt2kj1HB0dERISgidPnkh/AI8aNQpr167F0qVLcfPmTcyfPx87d+6UroB4enrCw8MDHTp0wMGDBxETE4N9+/YhODhYIQZNTU1s2LABFStWRIMGDQr895WZmRkeP36MkJCQXNcXL14cOjo6WLx4MW7fvo09e/ZgypQpKh0fAwMD/P3339DS0kKzZs2QlJSE0qVLo3v37ujZsyd27tyJmJgYnDp1CjNmzMDff/8tHa+kpCSEhITg2bNnBb6VRC6XIzAwEL/88otCeffu3VGkSBG0adMGR44cQUxMDMLCwhAQEIAHDx4AyL6i4+HhgQ0bNkiJVIUKFZCamoqQkBBpSCQATJgwAX/++Sdu3bqFK1euYO/evXBxcVHp2HzIwIED8fz5c3Tt2hWnT59GdHQ09u/fj169eiEzMxNmZmawsLDAihUrcOvWLfz7778YPny4WmOgwqdyclWrVi04Oztj+/bt6NWrF+7evYuQkBD4+/vzGVhERESkFi8eJygkVgGbfkCJqg4K92D90nUFXjxOUGu/hoaGqFmzJhYsWAAPDw+UL18e48ePR58+ffDrr79K9ebNm4eDBw/C3t4elStXBgC0bdsWixYtwty5c+Hq6orly5djzZo1CldIduzYgerVq6Nr164oV64cRo8enesVHS0tLWzatAmurq5o0KBBga8wmZqa5jqkD8i+MrN27Vps27YN5cqVw8yZMzF37lwVjk42Q0ND7Nu3D0IItGjRAsnJyVizZg169uyJESNGoEyZMmjbti1Onz6N4sWLAwDq1KmDfv36wcfHB5aWlpg9e3aB+/P19UWJEiUUyvT19XH48GEUL14c7du3h4uLC/z9/ZGSkgJjY2OpnqenJzIzM6X3QENDAx4eHpDJZAr3x+no6GDs2LGoUKECPDw8oKmpic2bN6t8bPJTtGhRREREIDMzE02aNIGbmxuGDh0KU1NTaGhoQENDA5s3b8bZs2dRvnx5DBs2DHPmzFFrDFT4ZEIIocoGP//8M7p3757r5eb/FYmJiTAxMUFCQoLCF5SIiIgKLiUlBTExMXBycoJcrtrDfnOec/UqPklp8oqcK1pGFobov7Z3oTxImIj+W/I7X6mSG6icXOVIS0tDTEwMSpYsmetsNN8yJldERESf7lOSKyA7wUpJToWZrfLImBePEyA30GViRURqoa7kSuVhgW/evIG/vz/09fXh6uoqTQ05ePBgzJw5U9XmiIiIiHKlZyzPNbECADNbEyZWRPTVUTm5GjNmDC5cuICwsDCFrK5Ro0bYsmVLPlsSERERERH971J5PN/u3buxZcsW1KpVS+HBdq6urp/0oDgiIiIiIqJvmcpXrp4+fQorKyul8uTk5AI/RZyIiIiIiOh/jcrJVbVq1aRnFgBvnwq9atUq1K5dW32RERERERERfUNUHhY4ffp0NGvWDFevXkVGRgYWLVqEq1ev4tixYwgPDy+MGImIiIiIiL56Kl+5qlevHs6fP4+MjAy4ubnhwIEDsLKywvHjx1G1atXCiJGIiIiIiOir91EPqCpZsiRWrlyp7liIiIiIiIi+WQW6cpWYmFjghYiIiIi+Tnfu3IFMJsP58+e/dChqERYWBplMhpcvX37pUIgAFDC5MjU1hZmZWYEWIiIiInVISEvDg+TkXNc9SE5GQlpaofTr5+cHmUymtDRt2rRQ+vuc7O3t8fjxY5QvX/6jts9JznIWIyMjuLq6YuDAgbh586aao/3vKWiymFPP1dUVmZmZCutMTU2xdu3awguS8lWgYYGhoaHSv+/cuYMxY8bAz89Pmh3w+PHjWLduHWbMmFE4URIREdF/SkJaGpr+ewhxKSkIa+wNewMDad395GR4HdwPK7kcwQ0awURHR+39N23aFGvWrFEo09XVVXs/n5umpiZsbGw+uZ1Dhw7B1dUVr1+/xqVLl7Bo0SJUrFgRf/31Fxo2bKiGSKkgbt++jfXr16NXr15fOhT6fwW6cuXp6Skt69evx/z58zFjxgy0bt0arVu3xowZMzB37lylkxARERHRx3iVno64lBTcTkqC18H9uP//V7ByEqvbSUmIS0nBq/T0QulfV1cXNjY2Csu7I3RevnyJvn37wtraGnK5HOXLl8fevXul9Tt27ICrqyt0dXXh6OiIefPmKbTv6OiI6dOno3fv3jAyMkLx4sWxYsUKhTqXLl1CgwYNoKenBwsLC/zwww9ISkqS1vv5+aFt27aYPn06rK2tYWpqismTJyMjIwOjRo2Cubk5ihUrpvD3WW7DAq9cuYKWLVvC2NgYRkZGcHd3R3R0dL7Hx8LCAjY2NihRogTatGmDQ4cOoWbNmvD391e4kvLnn3+iSpUqkMvlKFGiBAIDA5GRkQEA6NatG3x8fBTaTU9PR5EiRbB+/XoAQFZWFmbMmAEnJyfo6emhYsWK2L59e76xFeTYT5kyBV27doWBgQHs7OywZMkShToymQzLly9Hy5Ytoa+vDxcXFxw/fhy3bt2Cl5cXDAwMUKdOHaXjlN/+5rS7atUqtGvXDvr6+ihVqhT27NkDIPu9qV+/PgDAzMwMMpkMfn5++e7r4MGDMXHiRKSmpuZZ5+XLl/j+++9haWkJY2NjNGjQABcuXAAAJCQkQFNTE2fOnAGQfbzNzc1Rq1Ytafs//vgD9vb2AIC0tDQMGjQItra2kMvlcHBwyPfiysd8RgHg/v376Ny5M0xNTWFubo42bdrgzp070vrTp0+jcePGKFKkCExMTODp6YnIyEiFNvI71oVKqEhPT0/cuHFDqTwqKkro6emp2txXKSEhQQAQCQkJXzoUIiKib9abN2/E1atXxZs3bz5q+3tJSaLErh0CQetEiV07RERcrMLre0lJao44m6+vr2jTpk2e6zMzM0WtWrWEq6urOHDggIiOjhZ//fWX+Oeff4QQQpw5c0ZoaGiIyZMni6ioKLFmzRqhp6cn1qxZI7Xh4OAgzM3NxZIlS8TNmzfFjBkzhIaGhrh+/boQQoikpCRha2sr2rdvLy5duiRCQkKEk5OT8PX1VYjTyMhIDBw4UFy/fl38/vvvAoDw9vYW06ZNEzdu3BBTpkwR2tra4v79+0IIIWJiYgQAce7cOSGEEA8ePBDm5uaiffv24vTp0yIqKkqsXr1aiuN972//rl27dgkA4uTJk0IIIQ4fPiyMjY3F2rVrRXR0tDhw4IBwdHQUkyZNEkIIsXfvXqGnpydevXoltfHXX38JPT09kZiYKIQQYurUqaJs2bIiODhYREdHizVr1ghdXV0RFhYmhBAiNDRUABAvXrxQ6dgbGRmJGTNmiKioKPHLL78ITU1NceDAAakOAGFnZye2bNkioqKiRNu2bYWjo6No0KCBCA4OFlevXhW1atUSTZs2lbb50P7mtFusWDGxceNGcfPmTREQECAMDQ1FfHy8yMjIEDt27BAARFRUlHj8+LF4+fJlru9Dzn4/fPhQ2Nraijlz5kjrTExMFPa3UaNGolWrVuL06dPixo0bYsSIEcLCwkLEx8cLIYSoUqWKtP358+eFubm50NHRkd6X77//XnTv3l0IIcScOXOEvb29OHz4sLhz5444cuSI2LhxY64xCvFxn9G0tDTh4uIievfuLS5evCiuXr0qunXrJsqUKSNSU1OFEEKEhISIoKAgce3aNXH16lXh7+8vrK2tpc/Nh451bvI7X6mSG6icXJUuXVqMGjVKqXzUqFGidOnSqjb3VWJyRURE9Ok+NbkSQjHBylkKM7ESIvsPQk1NTWFgYKCwTJs2TQghxP79+4WGhoaIiorKdftu3bqJxo0bK5SNGjVKlCtXTnrt4OAgvvvuO+l1VlaWsLKyEkuXLhVCCLFixQphZmYmkt7Zz7///ltoaGiIJ0+eSHE6ODiIzMxMqU6ZMmWEu7u79DojI0MYGBiITZs2CSGUk6OxY8cKJycnkZaWVqBjk19yde3aNQFAbNmyRQghRMOGDcX06dMV6gQFBQlbW1shhBDp6emiSJEiYv369dL6rl27Ch8fHyGEECkpKUJfX18cO3ZMoQ1/f3/RtWtXIYRyclXQY/9uUiSEED4+PqJZs2bSawBi3Lhx0uvjx48LAOL333+XyjZt2iTkcrn0+kP7m1u7SUlJAoDYt29frvuTl3frLVu2TJibm0uJ2LvJ1ZEjR4SxsbFISUlR2L5kyZJi+fLlQgghhg8fLlq0aCGEEGLhwoXCx8dHVKxYUYrJ2dlZrFixQgghxODBg0WDBg1EVlZWvvHl+JjPaFBQkChTpoxCH6mpqUJPT0/s378/134yMzOFkZGR+Ouvv6SyDx3r96kruVL5OVcLFizA4sWL4ebmhu+//x7ff/89KlSogMWLF2PBggUffQWNiIiI6H32BgYIqltPoSyobj2Fe7AKQ/369XH+/HmFpV+/fgCA8+fPo1ixYihdunSu2167dg1169ZVKKtbty5u3rypMGSuQoUK0r9lMhlsbGwQFxcntVGxYkUYvLOfdevWRVZWFqKioqQyV1dXaGi8/XPO2toabm5u0mtNTU1YWFhI7b7v/PnzcHd3h7a29gePyYdk/z2bvS8AcOHCBUyePBmGhobS0qdPHzx+/BivX7+GlpYWOnfujA0bNgAAkpOT8eeff6J79+4AgFu3buH169do3LixQhvr16/Pc9hiQY99zrwB776+du2aQtm774+1tTUAKBxba2trpKSkSLNlf2h/c2vXwMAAxsbGeb4/BeHv7w8LCwvMmjVLad2FCxeQlJQECwsLhbhiYmKkY+jp6YmjR48iMzMT4eHh8PLygpeXF8LCwvDo0SNpKCSQPczv/PnzKFOmDAICAnDgwIEPxqfqZ/TChQu4desWjIyMpHjNzc2RkpIixRwbG4s+ffqgVKlSMDExgbGxMZKSknDv3j2FvtV9rAtC5edcNW/eHDdv3sTSpUulD2GrVq3Qr18/aTwmERERkTrcT05Gj4ijCmU9Io4qTXKhbgYGBnB2ds51nZ6enlr6eD+hkclkyMrK+uQ2VGlXXfsCQPq70MnJCQCQlJSEwMBAtG/fXqmuXC4HAHTv3h2enp6Ii4vDwYMHoaenJ83KmHN/2d9//w07OzuF7T/H5CLvHsechDG3spxjW5D9fb+NnHZUfd/fpaWlhWnTpsHPzw+DBg1SWJeUlARbW1uEhYUpbWdqagoA8PDwwKtXrxAZGYnDhw9j+vTpsLGxwcyZM1GxYkUULVoUpUqVAgBUqVIFMTEx2LdvHw4dOoTOnTujUaNG+d4Hp+pnNCkpCVWrVpWS7ndZWloCAHx9fREfH49FixbBwcEBurq6qF27NtLem0FU3ce6ID7qIcLFihXDtGnT1B0LERERkeTdyStKGBoiqG499Ig4Kk1yUdgJVl4qVKiABw8e4MaNG7levXJxcUFERIRCWUREBEqXLg1NTc0C9eHi4oK1a9ciOTlZunoVEREBDQ0NlClT5tN34v9VqFAB69atQ3p6+iddvcrKysIvv/wCJycnVK5cGUD2H+JRUVF5JqkAUKdOHdjb22PLli3Yt28fOnXqJMVRrlw56Orq4t69e/D09CxQHAU99idOnFCoc+LECbi4uBSoj7wUZH8/ROf/Z758f3r1D+nUqRPmzJmDwMBApZiePHkCLS0tODo65rqtqakpKlSogF9//RXa2tooW7YsrKys4OPjg7179yode2NjY/j4+MDHxwcdO3ZE06ZN8fz5c5ibm6sUc16qVKmCLVu2wMrKCsbGxrnWiYiIwG+//YbmzZsDyJ4A49mzZ2rp/1OpPCyQiIiIqLA9eC+xCmvsjTqWVghr7I0ShoZSgpXXc7A+VWpqKp48eaKw5Pzx5unpCQ8PD3To0AEHDx6UfskPDg4GAIwYMQIhISGYMmUKbty4gXXr1uHXX3/FyJEjC9x/9+7dIZfL4evri8uXLyM0NBSDBw9Gjx49pCFq6jBo0CAkJiaiS5cuOHPmDG7evImgoCCFoYe5iY+Px5MnT3D79m3s2bMHjRo1wqlTp/D7779LScyECROwfv16BAYG4sqVK7h27Ro2b96McePGKbTVrVs3LFu2DAcPHpSGBAKAkZERRo4ciWHDhmHdunWIjo5GZGQkFi9ejHXr1uUaV0GPfUREBGbPno0bN25gyZIl2LZtG4YMGfIxh1BS0P3Nj4ODA2QyGfbu3YunT58qzA75ITNnzsTq1auR/M53olGjRqhduzbatm2LAwcO4M6dOzh27Bh+/vlnaYZAAPDy8sKGDRukRMrc3BwuLi7YsmWLQnI1f/58bNq0CdevX8eNGzewbds22NjYSFfB1KF79+4oUqQI2rRpgyNHjiAmJgZhYWEICAjAgwcPAAClSpVCUFAQrl27hpMnT6J79+5qvQr7KZhcERER0VfHSFsbVnK5lFjlXKGyNzCQEiwruRxGarhXKDfBwcGwtbVVWOrVe3vv144dO1C9enV07doV5cqVw+jRo6WrDVWqVMHWrVuxefNmlC9fHhMmTMDkyZM/OK32u/T19bF//348f/4c1atXR8eOHdGwYUP8+uuvat1PCwsL/Pvvv0hKSoKnpyeqVq2KlStXfvAqVqNGjWBraws3NzeMGTMGLi4uuHjxojSVOAB4e3tj7969OHDgAKpXr45atWphwYIFcHBwUGire/fuuHr1Kuzs7JTul5oyZQrGjx+PGTNmwMXFBU2bNsXff/8tDT18X0GP/YgRI3DmzBlUrlwZU6dOxfz58+Ht7a3CkVNW0P3Nj52dHQIDAzFmzBhYW1srDfPLT4MGDdCgQQOlqd//+ecfeHh4oFevXihdujS6dOmCu3fvKiTpnp6eyMzMlO6tArITrvfLjIyMMHv2bFSrVg3Vq1fHnTt38M8//yjcU/Wp9PX1cfjwYRQvXhzt27eHi4sL/P39kZKSIl3J+v333/HixQtUqVIFPXr0QEBAAKysrNQWw6eQiZy7D0mSmJgIExMTJCQk5Hk5koiIiPKXkpKCmJgYODk5KdxzUlAJaWl4lZ6OYrkM/XuQnAwjbe1CeYAw/W9zdHTE0KFDMXTo0C8dCn1F8jtfqZIbfPErV0uWLIGjoyPkcjlq1qyJU6dO5Vt/27ZtKFu2LORyOdzc3PDPP/8o1bl27Rpat24NExMTGBgYoHr16kqzhxAREdHXzURHJ9fECgCKGRgwsSKir84XTa62bNmC4cOHY+LEiYiMjETFihXh7e2d5xSJx44dQ9euXeHv749z586hbdu2aNu2LS5fvizViY6ORr169VC2bFmEhYXh4sWLGD9+/Ef9YkZERERERFRQBRoWWLlyZWm6yQ+JjIwscOc1a9ZE9erVpfHDWVlZsLe3x+DBgzFmzBil+j4+PkhOTsbevXulslq1aqFSpUpYtmwZAKBLly7Q1tZGUFBQgeN4H4cFEhERfbpPHRZIRPS5fNZhgW3btkWbNm3Qpk0beHt7Izo6Grq6utJDxuRyOaKjo1W6ETAtLQ1nz55Fo0aN3gajoYFGjRrh+PHjuW5z/PhxhfpA9s2DOfWzsrLw999/o3Tp0vD29oaVlRVq1qyJ3bt35xtLamoqEhMTFRYiIiIiIiJVFOg5VxMnTpT+/f333yMgIABTpkxRqnP//v0Cd/zs2TNkZmYqTSdqbW2N69ev57rNkydPcq3/5MkTAEBcXBySkpIwc+ZMTJ06FbNmzUJwcDDat2+P0NDQPJ+RMGPGDKXnAhAREREREalC5Xuutm3bhp49eyqVf/fdd9ixY4dagvpYOU9cbtOmDYYNG4ZKlSphzJgxaNmypTRsMDdjx45FQkKCtKiSJBIREREREQEfkVzp6ekpPfkayH4YmyrjqYsUKQJNTU3ExsYqlMfGxsLGxibXbWxsbPKtX6RIEWhpaaFcuXIKdVxcXPKdLVBXVxfGxsYKCxERERERkSpUTq6GDh2K/v37IyAgAH/88Qf++OMPDB48GAMHDsSwYcMK3I6Ojg6qVq2KkJAQqSwrKwshISGoXbt2rtvUrl1boT4AHDx4UKqvo6OD6tWrKz1V/MaNGyo9wI2IiIiIiEhVBbrn6l1jxoxBiRIlsGjRIvzxxx8Asq8MrVmzBp07d1apreHDh8PX1xfVqlVDjRo1sHDhQiQnJ6NXr14AgJ49e8LOzg4zZswAAAwZMgSenp6YN28eWrRogc2bN+PMmTNYsWKF1OaoUaPg4+MDDw8P1K9fH8HBwfjrr78QFham6q4SEREREREVmErJVUZGBqZPn47evXurnEjlxsfHB0+fPsWECRPw5MkTVKpUCcHBwdKkFffu3YOGxtuLa3Xq1MHGjRsxbtw4/PTTTyhVqhR2796N8uXLS3XatWuHZcuWYcaMGQgICECZMmWwY8cO1KtX75PjJSIiIiIiykuBnnP1LkNDQ1y+fBmOjo6FFNKXx+dcERERfbpPfs5VWhKQlgwYWiuvS4oFdAwAHcNPD/Q9fn5+WLdunVL5zZs34ezsnOd6b29vBAcHAwAcHR1x9+5dAIBcLoe1tTVq1KiBfv36oUGDBvn27+XlhfDwcGzatAldunSRyhcuXIiFCxfizp07n7B3RJSbz/qcq3c1bNgQ4eHhqm5GREREVHBpScA/g4G/fgCSniiuS3qSXf7P4Ox6haBp06Z4/PixwuLk5JTv+k2bNim0MXnyZDx+/BhRUVFYv349TE1N0ahRI0ybNu2D/cvlcowbNw7p6elq3zciKjwqJ1fNmjXDmDFjMHLkSGzatAl79uxRWIiIiIg+WVoy8OY58Ooh8FfftwlW0pPs168eZq9PSy6U7nV1dWFjY6OwaGpq5rvezMxMoQ0jIyPY2NigePHi8PDwwIoVKzB+/HhMmDBBafKt93Xt2hUvX77EypUr8633559/okqVKpDL5ShRogQCAwORkZEBABg5ciRatmwp1V24cCFkMpl0dQ0AnJ2dsWrVKgBAWFgYatSoAQMDA5iamqJu3brS1bf33blzBzKZDFu3boW7uzv09PRQvXp13LhxA6dPn0a1atVgaGiIZs2a4enTpwrbrlq1Ci4uLpDL5Shbtix+++03hfU//vgjSpcuDX19fZQoUQLjx49XSDInTZqESpUqISgoCI6OjjAxMUGXLl3w6tWrfI8V0eegcnI1YMAAxMbGYv78+ejevTvatm0rLe3atSuMGImIiOi/xtAaaLUcMLJ7m2A9ufA2sTKyy16f25DBr9iQIUMghMCff/6Zbz1jY2P8/PPPmDx5MpKTc08gjxw5gp49e2LIkCG4evUqli9fjrVr10pXxjw9PXH06FFkZmYCAMLDw1GkSBFpkq+HDx8iOjoaXl5eyMjIQNu2beHp6YmLFy/i+PHj+OGHHyCTyfKNc+LEiRg3bhwiIyOhpaWFbt26YfTo0Vi0aBGOHDmCW7duYcKECVL9DRs2YMKECZg2bRquXbuG6dOnY/z48QrDLI2MjLB27VpcvXoVixYtwsqVK7FgwQKFfqOjo7F7927s3bsXe/fuRXh4OGbOnJlvrESfg8rJVVZWVp5LzpeXiIiI6JMZ2igmWHv830uscn8upjrs3bsXhoaG0tKpU6d81xsaGmL69OkfbNfc3BxWVlYFum9qwIABkMvlmD9/fq7rAwMDMWbMGPj6+qJEiRJo3LgxpkyZguXLlwMA3N3d8erVK5w7dw5CCBw+fBgjRoyQkquwsDDY2dnB2dkZiYmJSEhIQMuWLVGyZEm4uLjA19cXxYsXzzfGkSNHwtvbGy4uLhgyZAjOnj2L8ePHo27duqhcuTL8/f0RGhoq1Z84cSLmzZuH9u3bw8nJCe3bt8ewYcOkmAFg3LhxqFOnDhwdHdGqVSuMHDkSW7duVeg3KysLa9euRfny5eHu7o4ePXooPa6H6EtQeSp2IiIios/G0AaoPzk7scpRf3KhJlYAUL9+fSxdulR6bWBgkO96IDtxKgghxAevCAHZQw8nT56MwYMHo3///krrL1y4gIiICIV7uDIzM5GSkoLXr1/D1NQUFStWRFhYGHR0dKCjo4MffvgBEydORFJSEsLDw+Hp6SnF7ufnB29vbzRu3BiNGjVC586dYWtrm2+MFSpUkP6dM9uzm5ubQllcXBwAIDk5GdHR0fD390efPn2kOhkZGTAxMZFeb9myBb/88guio6ORlJSEjIwMpUkEHB0dYWRkJL22tbWV+iH6kj4quUpOTkZ4eDju3buHtLQ0hXUBAQFqCYyIiIgISU+A0AmKZaETCv3KlYGBAZydnT96fV7i4+Px9OlThckx8vPdd99h7ty5mDp1qtJMzUlJSQgMDET79u2VtsuZ7czLywthYWHQ1dWFp6cnzM3N4eLigqNHjyI8PBwjRoyQtlmzZg0CAgIQHByMLVu2YNy4cTh48CBq1aqVZ3za2trSv3MSxvfLsrKypHgBYOXKlahZs6ZCOzn3sx0/fhzdu3dHYGAgvL29YWJigs2bN2PevHl59vt+P0RfksrJ1blz59C8eXO8fv0aycnJMDc3x7Nnz6Cvrw8rKysmV0RERKQe705eYWSXfcUqdMLbe7AKOcEqDIsWLYKGhgbatm1boPoaGhqYMWMG2rdvr3T1qkqVKoiKiso3yfP09MTq1auhpaWFpk2bAshOuDZt2oQbN27Ay8tLoX7lypVRuXJljB07FrVr18bGjRvzTa5UYW1tjaJFi+L27dvo3r17rnWOHTsGBwcH/Pzzz1JZXpNqEH2NVE6uhg0bhlatWmHZsmUwMTHBiRMnoK2tje+++w5DhgwpjBiJiIjovyYpNpfJK/7/Hqyc8r/6Aq1WfJFJLVJTU/HkieIU8VpaWihSpIj0+tWrV3jy5AnS09MRExODP/74A6tWrcKMGTNUuurVokUL1KxZE8uXL5eG3gHAhAkT0LJlSxQvXhwdO3aEhoYGLly4gMuXL2Pq1KkAAA8PD7x69Qp79+6VJnzw8vJCx44dYWtri9KlSwMAYmJisGLFCrRu3RpFixZFVFQUbt68iZ49e370McpNYGAgAgICYGJigqZNmyI1NRVnzpzBixcvMHz4cJQqVQr37t3D5s2bUb16dfz999/YtWuXWmMgKkwqT2hx/vx5jBgxAhoaGtDU1ERqairs7e0xe/Zs/PTTT4URIxEREf3X6BgAeubKk1e8O8mFnnl2vS8gODgYtra2Cku9evUU6kyYMAG2trZwdnZGjx49kJCQgJCQEPz4448q9zdr1iykpKQolHl7e2Pv3r04cOAAqlevjlq1amHBggVwcHCQ6piZmcHNzQ2WlpYoW7YsgOyEKysrS7rfCgD09fVx/fp1dOjQAaVLl8YPP/yAgQMHom/fvirHmp/vv/8eq1atwpo1a+Dm5gZPT0+sXbtWGibZunVrDBs2DIMGDUKlSpVw7NgxjB8/Xq0xEBUmmRBCqLKBpaUljh07hlKlSqF06dJYvHgxvL29cf36dVStWjXP6UK/Jao8hZmIiIhyl5KSgpiYGDg5OUn3AKkkLSn7OVa5XZlKis1OrHQMPz1QIvrPy+98pUpuoPKwwMqVK+P06dMoVaoUPD09MWHCBDx79gxBQUEoX768qs0RERER5U7HMO/k6Rt7vhUR/TeoPCxw+vTp0rSc06ZNg5mZGfr374+nT59ixYoVag+QiIiIiIjoW6Dylatq1apJ/7ayskJwcLBaAyIiIiIiIvoWqXzlioiIiIiIiJQV6MpV5cqVC/QkcQCIjIz8pICIiIiIiIi+RQVKrt590F1KSgp+++03lCtXDrVr1wYAnDhxAleuXMGAAQMKJUgiIiIiIqKvXYGSq4kTJ0r//v777xEQEIApU6Yo1bl//756oyMiIiIiIvpGqHzP1bZt23J9Wvd3332HHTt2qCUoIiIiIiKib43KyZWenh4iIiKUyiMiIj7uAYFERERERET/A1Sein3o0KHo378/IiMjUaNGDQDAyZMnsXr1aowfP17tARIRERF9zby8vFCpUiUsXLjwS4cicXR0xNChQzF06NAvHQrRf4rKV67GjBmDdevW4ezZswgICEBAQAAiIyOxZs0ajBkzpjBiJCIiov+gjMRkpDx+luu6lMfPkJGYXCj9+vn5QSaToV+/fkrrBg4cCJlMBj8/P6ls586dSveifw5eXl6QyWRKS0ZGBk6fPo0ffvjhs8ekbmFhYZDJZHB1dUVmZqbCOlNTU6xdu/bLBEaUh496zlXnzp0RERGB58+f4/nz54iIiEDnzp3VHRsRERH9R2UkJuOc3wREdhmDlEdPFdalPHqKyC5jcM5vQqElWPb29ti8eTPevHnztt+UFGzcuBHFixdXqGtubg4jI6OP6kcIgYyMjI+Os0+fPnj8+LHCoqWlBUtLS+jr6390u1+b27dvY/369V86DKIP+uiHCKelpeHBgwe4d++ewkJERET0qTKS3yA9PgFv7j1BZNexUoKV8ugpIruOxZt7T5Aen4CM5DcfaOnjVKlSBfb29ti5c6dUtnPnThQvXhyVK1dWqOvl5aUw/C41NRU//vgj7O3toaurC2dnZ/z+++8A3l6J2bdvH6pWrQpdXV0cPXoUqampCAgIgJWVFeRyOerVq4fTp09/ME59fX3Y2NgoLED2sMB3hynKZDKsWrUK7dq1g76+PkqVKoU9e/ZI6zMzM+Hv7w8nJyfo6emhTJkyWLRokUJffn5+aNu2LebOnQtbW1tYWFhg4MCBSE9PL9C+A8Dly5fRrFkzGBoawtraGj169MCzZ7lfnXzX4MGDMXHiRKSmpuZZ5+XLl/j+++9haWkJY2NjNGjQABcuXAAAJCQkQFNTE2fOnAEAZGVlwdzcHLVq1ZK2/+OPP2Bvbw8g++/cQYMGwdbWFnK5HA4ODpgxY0aefeccm+nTp8Pa2hqmpqaYPHkyMjIyMGrUKJibm6NYsWJYs2aNwnb3799H586dYWpqCnNzc7Rp0wZ37tyR1p8+fRqNGzdGkSJFYGJiAk9PT6Vnyn7ovaXPS+Xk6ubNm3B3d4eenh4cHBzg5OQEJycnODo6wsnJqTBiJCIiov8YuW0RVNk0A3rFbaQE6+XZa1JipVfcBlU2zYDctkihxdC7d2+FP4ZXr16NXr16fXC7nj17YtOmTfjll19w7do1LF++HIaGhgp1xowZg5kzZ+LatWuoUKECRo8ejR07dmDdunWIjIyEs7MzvL298fz5c7XtT2BgIDp37oyLFy+iefPm6N69u9R+VlYWihUrhm3btuHq1auYMGECfvrpJ2zdulWhjdDQUERHRyM0NBTr1q3D2rVrFYbm5bfvL1++RIMGDVC5cmWcOXMGwcHBiI2NLdDop6FDhyIjIwOLFy/Os06nTp0QFxeHffv24ezZs6hSpQoaNmyI58+fw8TEBJUqVUJYWBgA4NKlS5DJZDh37hySkpIAAOHh4fD09AQA/PLLL9izZw+2bt2KqKgobNiwAY6OjvnG+O+//+LRo0c4fPgw5s+fj4kTJ6Jly5YwMzPDyZMn0a9fP/Tt2xcPHjwAAKSnp8Pb2xtGRkY4cuQIIiIiYGhoiKZNmyItLQ0A8OrVK/j6+uLo0aM4ceIESpUqhebNm+PVq1cKfef33tJnJlRUp04d4eHhIf755x9x7tw5cf78eYXlf0FCQoIAIBISEr50KERERN+sN2/eiKtXr4o3b958fBsP40SEh7845NhCWiI8/MWbh3FqjFSRr6+vaNOmjYiLixO6urrizp074s6dO0Iul4unT5+KNm3aCF9fX6m+p6enGDJkiBBCiKioKAFAHDx4MNe2Q0NDBQCxe/duqSwpKUloa2uLDRs2SGVpaWmiaNGiYvbs2XnG6enpKbS1tYWBgYG0DB8+XAghhIODg1iwYIFUF4AYN26cQp8AxL59+/Jsf+DAgaJDhw4Kx8XBwUFkZGRIZZ06dRI+Pj4F2vcpU6aIJk2aKJTdv39fABBRUVG5bpNzvF68eCGWLVsmzM3NxcuXL4UQQpiYmIg1a9YIIYQ4cuSIMDY2FikpKQrblyxZUixfvlwIIcTw4cNFixYthBBCLFy4UPj4+IiKFStKx8DZ2VmsWLFCCCHE4MGDRYMGDURWVlaex+ddOccmMzNTKitTpoxwd3eXXmdkZAgDAwOxadMmIYQQQUFBokyZMgp9pKamCj09PbF///5c+8nMzBRGRkbir7/+kso+5r0lZfmdr1TJDVSeLfD8+fM4e/YsypYtq7YEj4iIiCg38qKWKDd/BM52HCWVlZs/AvKiloXet6WlJVq0aIG1a9dCCIEWLVqgSJH8r5SdP38empqa0hWQvFSrVk36d3R0NNLT01G3bl2pTFtbGzVq1MC1a9fybad79+74+eefpdempqZ51q1QoYL0bwMDAxgbGyMuLk4qW7JkCVavXo179+7hzZs3SEtLQ6VKlRTacHV1haampvTa1tYWly5dAvDhfb9w4QJCQ0OVruIB2cegdOnS+e6rv78/5s2bh1mzZmH69OlKbSclJcHCwkKh/M2bN4iOjgYAeHp64vfff0dmZibCw8PRpEkT2NjYICwsDBUqVMCtW7fg5eUFIHuYX+PGjVGmTBk0bdoULVu2RJMmTfKNz9XVFRoabweFWVtbo3z58tJrTU1NWFhYSMf8woULuHXrltL9eikpKVLMsbGxGDduHMLCwhAXF4fMzEy8fv1a6VacD7239PmonFyVK1euQGNjiYiIiD5VyqOnuDp8nkLZ1eHzsocEfoYEq3fv3hg0aBCA7OTjQ/T09ArUroGBwSfFlcPExATOzs4Fqqutra3wWiaTISsrCwCwefNmjBw5EvPmzUPt2rVhZGSEOXPm4OTJkwVu40P7npSUhFatWmHWrFlK62xtbT8Yv5aWFqZNmwY/Pz/pPXm3bVtbW2nY37tyEk4PDw+8evUKkZGROHz4MKZPnw4bGxvMnDkTFStWRNGiRVGqVCkA2ffcxcTEYN++fTh06BA6d+6MRo0aYfv27XnGl9uxye94JSUloWrVqtiwYYNSW5aW2Z9tX19fxMfHY9GiRXBwcICuri5q164tDRvMr++cfujzUvmeq1mzZmH06NEICwtDfHw8EhMTFRYiIiIidXh38gq94jaoun2Owj1Y788iWBhy7n/JuT/mQ9zc3JCVlYXw8PAC91GyZEno6OggIiJCKktPT8fp06dRrly5j4pbVREREahTpw4GDBiAypUrw9nZWbp6UlAf2vcqVargypUrcHR0hLOzs8JS0GSzU6dOcHV1RWBgoFLbT548gZaWllLbOVcbTU1NUaFCBfz666/Q1tZG2bJl4eHhgXPnzmHv3r1KV9yMjY3h4+ODlStXYsuWLdixY4da72OqUqUKbt68CSsrK6WYTUxMAGS/LwEBAWjevDlcXV2hq6vLixxfOZWTq0aNGuHEiRNo2LAhrKysYGZmBjMzM5iamsLMzKwwYiQiIqL/mJTHz5QmrzCt6qI0yUVez8FSF01NTVy7dg1Xr15VGA6XF0dHR/j6+qJ3797YvXs3YmJiEBYWpjQxxLsMDAzQv39/jBo1CsHBwbh69Sr69OmD169fw9/fX527k6dSpUrhzJkz2L9/P27cuIHx48cXaLbCd31o3wcOHIjnz5+ja9euOH36NKKjo7F//3706tVL6RlW+Zk5cyZWr16N5OS30/A3atQItWvXRtu2bXHgwAHcuXMHx44dw88//yzNEAhkz+y4YcMGKZEyNzeHi4sLtmzZopBczZ8/H5s2bcL169dx48YNbNu2DTY2NvkOu1RV9+7dUaRIEbRp0wZHjhyRjldAQIA06UWpUqUQFBSEa9eu4eTJk+jevXuBr47Sl6FychUaGorQ0FD8+++/CktOGREREdGn0jLQg7aFydtZAf9/CKC8qKWUYGlbmEDLoPD/0DQ2NoaxsXGB6y9duhQdO3bEgAEDULZsWfTp00chEcjNzJkz0aFDB/To0QNVqlTBrVu3sH///s/2w3Xfvn3Rvn17+Pj4oGbNmoiPj8eAAQNUbie/fS9atCgiIiKQmZmJJk2awM3NDUOHDoWpqanCvUof0qBBAzRo0EDh+WAymQz//PMPPDw80KtXL5QuXRpdunTB3bt3YW1tLdXz9PREZmamdG8VkJ1wvV9mZGSE2bNno1q1aqhevTru3LmDf/75R6U4P0RfXx+HDx9G8eLF0b59e7i4uMDf3x8pKSnS5+3333/HixcvUKVKFfTo0UOarp++XjIhhPjSQXxtEhMTYWJigoSEBJVOpkRERPRWSkoKYmJi4OTkBLlcrvL2GYnJyEh+k+t06ymPn0HLQA9axuq5d4mI/tvyO1+pkhuoPKFFjpyZSt6/oe7d2UqIiIiIPpaWsUGeyVNhPt+KiOhjqZxcPX36FL169cK+fftyXa/KmFkiIiIiIqL/FSoPHB06dChevnyJkydPQk9PD8HBwVi3bh1KlSqFPXv2FEaMREREREREXz2Vr1z9+++/+PPPP1GtWjVoaGjAwcEBjRs3hrGxMWbMmIEWLVoURpxERERERERfNZWvXCUnJ0uzlJiZmeHp0+xnTLi5uSEyMlK90REREREREX0jVE6uypQpg6ioKABAxYoVsXz5cjx8+BDLli0r0NO1iYiIiIiI/hepPCxwyJAhePz4MQBg4sSJaNq0KTZs2AAdHR2sXbtW3fERERERERF9E1ROrr777jvp31WrVsXdu3dx/fp1FC9eHEWKcFpUIiIiIiL6b1J5WODkyZPx+vVr6bW+vj6qVKkCAwMDTJ48Wa3BERERERERfStUTq4CAwORlJSkVP769WsEBgaqJSgiIiIiUj8/Pz+0bdv2s/fr6OiIhQsXfvZ+3+fl5YWhQ4d+6TDof5jKyZUQAjKZTKn8woULMDc3V0tQRERERElJSYiNjc11XWxsbK4/9qrDxyQgMpkMu3fvLpR4PsadO3cgk8lw/vx5hfJFixZ9dffIOzo6QiaT5bn4+fl96RAV5MR74sQJhfKhQ4fCy8vrywRFX40C33NlZmYmfchLly6tkGBlZmYiKSkJ/fr1K5QgiYiI6L8lKSkJgwcPxvPnz7F8+XLY2NhI6548eYK+ffvC3NwcixcvhqGh4ReMVL3S09Ohra1daO2bmJgUWtsf6/Tp08jMzAQAHDt2DB06dEBUVBSMjY0BAHp6egr1C/sYFYRcLsePP/6I8PDwLxoHfX0KfOVq4cKFmD9/PoQQCAwMxIIFC6Rl2bJlOHr0KJYsWVKYsRIREdF/RHJyMp4/f46HDx+ib9++ePLkCYC3idXDhw/x/PlzJCcnF3osXl5eCAgIwOjRo2Fubg4bGxtMmjRJWu/o6AgAaNeuHWQymfQaAP78809UqVIFcrkcJUqUQGBgIDIyMqT1MpkMS5cuRevWrWFgYIBp06Zh0qRJqFSpEoKCguDo6AgTExN06dIFr169krYLDg5GvXr1YGpqCgsLC7Rs2RLR0dHSeicnJwBA5cqVIZPJpCsq71+VS01NRUBAAKysrCCXy1GvXj2cPn1aWh8WFgaZTIaQkBBUq1YN+vr6qFOnjvRYHgCIjo5GmzZtYG1tDUNDQ1SvXh2HDh0q8PG1tLSEjY0NbGxspFFQVlZWsLGxQUpKCkxNTbFlyxZ4enpCLpdjw4YNiI+PR9euXWFnZwd9fX24ublh06ZNCu0mJyejZ8+eMDQ0hK2tLebNm6fUd2pqKkaOHAk7OzsYGBigZs2aCAsL+2DMP/zwA06cOIF//vkn33qrVq2Ci4sL5HI5ypYti99++01a17FjRwwaNEh6PXToUMhkMly/fh0AkJaWBgMDA+lYbt++HW5ubtDT04OFhQUaNWqU5+c/533bv38/KleuDD09PTRo0ABxcXHYt28fXFxcYGxsjG7duinMpZCVlYUZM2bAyckJenp6qFixIrZv3y6tz8zMhL+/v7S+TJkyWLRokULfOZ+xuXPnwtbWFhYWFhg4cCDS09M/eFz/JwgVhYWFifT0dFU3+6YkJCQIACIhIeFLh0JERPTNevPmjbh69ap48+bNR23/+PFj0bp1a1G1alXRunVrcf78eYXXjx8/VnPE2Xx9fUWbNm2k156ensLY2FhMmjRJ3LhxQ6xbt07IZDJx4MABIYQQcXFxAoBYs2aNePz4sYiLixNCCHH48GFhbGws1q5dK6Kjo8WBAweEo6OjmDRpktQ2AGFlZSVWr14toqOjxd27d8XEiROFoaGhaN++vbh06ZI4fPiwsLGxET/99JO03fbt28WOHTvEzZs3xblz50SrVq2Em5ubyMzMFEIIcerUKQFAHDp0SDx+/FjEx8fnum8BAQGiaNGi4p9//hFXrlwRvr6+wszMTKofGhoqAIiaNWuKsLAwceXKFeHu7i7q1KkjtXH+/HmxbNkycenSJXHjxg0xbtw4IZfLxd27d6U6Dg4OYsGCBR889jn9vXjxQgghRExMjAAgHB0dxY4dO8Tt27fFo0ePxIMHD8ScOXPEuXPnRHR0tPjll1+EpqamOHnypNRW//79RfHixcWhQ4fExYsXRcuWLYWRkZEYMmSIVOf7778XderUEYcPHxa3bt0Sc+bMEbq6uuLGjRt5xpizLwEBAaJChQrSMR8yZIjw9PSU6v3xxx/C1tZWinvHjh3C3NxcrF27VgghxC+//CJcXV2l+pUqVRJFihQRS5cuFUIIcfToUaGtrS2Sk5PFo0ePhJaWlpg/f76IiYkRFy9eFEuWLBGvXr3K9zjWqlVLHD16VERGRgpnZ2fh6ekpmjRpIiIjI8Xhw4eFhYWFmDlzprTd1KlTRdmyZUVwcLCIjo4Wa9asEbq6uiIsLEwIIURaWpqYMGGCOH36tLh9+7b4448/hL6+vtiyZYvUhq+vrzA2Nhb9+vUT165dE3/99ZfQ19cXK1asyO+t/+LyO1+pkhuonFydPXtWXLx4UXq9e/du0aZNGzF27FiRmpqqanNfJSZXREREn+5TkyshFBOsnKUwEyshck+u6tWrp1CnevXq4scff5ReAxC7du1SqNOwYUMxffp0hbKgoCBha2ursN3QoUMV6kycOFHo6+uLxMREqWzUqFGiZs2aecb89OlTAUBcunRJCPE2KTl37lye+5aUlCS0tbXFhg0bpPVpaWmiaNGiYvbs2UKIt3+kHzp0SKrz999/CwD5vq+urq5i8eLF0utPTa4WLlz4wW1btGghRowYIYQQ4tWrV0JHR0ds3bpVWh8fHy/09PSk5Oru3btCU1NTPHz4UKGdhg0birFjx+bZT86+xMXFCSMjI7F+/XohhHJyVbJkSbFx40aFbadMmSJq164thBDi4sWLQiaTibi4OPH8+XOho6MjpkyZInx8fIQQ2YlOThJ79uxZAUDcuXPng8dBiNzftxkzZggAIjo6Wirr27ev8Pb2FkIIkZKSIvT19cWxY8cU2vL39xddu3bNs6+BAweKDh06SK99fX2Fg4ODyMjIkMo6deok7dfXSl3JlcoTWvTt2xc3btwAANy+fRs+Pj7Q19fHtm3bMHr06E+4hkZERESkyMbGRulRL5MnT1a4B+tzqFChgsJrW1tbxMXF5bvNhQsXMHnyZBgaGkpLnz598PjxY4WhWNWqVVPa1tHREUZGRnn2d/PmTXTt2hUlSpSAsbGxNBTx3r17Bd6n6OhopKeno27dulKZtrY2atSogWvXrinUfXf/bW1tAUCKJykpCSNHjoSLiwtMTU1haGiIa9euqRTLh7x/jDIzMzFlyhS4ubnB3NwchoaG2L9/v9RndHQ00tLSULNmTWkbc3NzlClTRnp96dIlZGZmonTp0grvUXh4uMIQy7xYWlpi5MiRmDBhAtLS0hTWJScnIzo6Gv7+/gptT506VWq7fPnyMDc3R3h4OI4cOYLKlSujZcuW0n1c4eHh0nDOihUromHDhnBzc0OnTp2wcuVKvHjx4oMxvvu+WVtbQ19fHyVKlFAoy3kfb926hdevX6Nx48YKMa9fv17heCxZsgRVq1aFpaUlDA0NsWLFCqX32tXVFZqamtLrgnxf/leo/BDhGzduoFKlSgCAbdu2wdPTExs3bkRERAS6dOnyVUyzSURERP8bnjx5ggkTJiiUTZgwQWmSi8L2/gQKMpkMWVlZ+W6TlJSEwMBAtG/fXmmdXC6X/m1gYKByf61atYKDgwNWrlyJokWLIisrC+XLl1f6I19d3o0nZ1KznHhGjhyJgwcPYu7cuXB2doaenh46duyo1ljeP0Zz5szBokWLsHDhQri5ucHAwABDhw5Vqc+kpCRoamri7NmzCokAgAJPkjJ8+HD89ttvCvdS5bQNACtXrlRI8ABIfclkMnh4eCAsLAy6urrw8vJChQoVkJqaisuXL+PYsWMYOXKktM3Bgwdx7NgxHDhwAIsXL8bPP/+MkydPSvfX5eb99y2/z1VOzH///Tfs7OwU6unq6gIANm/ejJEjR2LevHmoXbs2jIyMMGfOHJw8eTLPft/v53/dR03FnnNwDh06hObNmwMA7O3t8ezZM/VGR0RERP9Z705eYWdnh99//x12dnZKk1x8DbS1taUZ73JUqVIFUVFRcHZ2Vlo0NFT+E0wSHx+PqKgojBs3Dg0bNoSLi4vSVQwdHR0AUIrpXSVLloSOjg4iIiKksvT0dJw+fRrlypUrcDwRERHw8/NDu3bt4ObmBhsbG9y5c0e1nVJRREQE2rRpg++++w4VK1ZEiRIlpJFVQPa+aWtrK/zR/+LFC4U6lStXRmZmJuLi4pTen4Im7oaGhhg/fjymTZumMOGItbU1ihYtitu3byu1/W4y5OnpibCwMISFhcHLywsaGhrw8PDAnDlzkJqaqnBVUSaToW7duggMDMS5c+ego6ODXbt2fdTxy025cuWgq6uLe/fuKcVsb28PIPu416lTBwMGDEDlypXh7OxcoKt8/yUqX7mqVq0apk6dikaNGiE8PBxLly4FAMTExMDa2lrtARIREdF/T2xsrEJilXOlavny5VJ53759sWLFiq/i7w9HR0eEhISgbt260NXVhZmZGSZMmICWLVuiePHi6NixIzQ0NHDhwgVcvnwZU6dO/ei+zMzMYGFhgRUrVsDW1hb37t3DmDFjFOpYWVlBT08PwcHBKFasGORyudI07AYGBujfvz9GjRoFc3NzFC9eHLNnz8br16/h7+9f4HhKlSqFnTt3olWrVpDJZBg/fnyhX6UoVaoUtm/fjmPHjsHMzAzz589HbGyslBQaGhrC398fo0aNgoWFBaysrPDzzz8rJLWlS5dG9+7d0bNnT8ybNw+VK1fG06dPERISggoVKqBFixYFiuWHH37AggULsHHjRoWrVIGBgQgICICJiQmaNm2K1NRUnDlzBi9evMDw4cMBZM9EOWzYMOjo6KBevXpS2ciRI1G9enXpit3JkycREhKCJk2awMrKCidPnsTTp0/h4uKiluMJAEZGRhg5ciSGDRuGrKws1KtXDwkJCYiIiICxsTF8fX1RqlQprF+/Hvv374eTkxOCgoJw+vTpfK+e/deo/LPJwoULERkZiUGDBuHnn3+Gs7MzgOzpIevUqaP2AImIiOi/x8DAAObm5gqJFQApwbKzs4O5uXmuQ+q+hHnz5uHgwYOwt7dH5cqVAQDe3t7Yu3cvDhw4gOrVq6NWrVpYsGABHBwcPqkvDQ0NbN68GWfPnkX58uUxbNgwzJkzR6GOlpYWfvnlFyxfvhxFixZFmzZtcm1r5syZ6NChA3r06IEqVarg1q1b2L9/P8zMzAocz/z582FmZoY6deqgVatW8Pb2RpUqVT5pHz9k3LhxqFKlCry9veHl5QUbGxulBz/PmTMH7u7uaNWqFRo1aoR69eqhatWqCnXWrFmDnj17YsSIEShTpgzatm2L06dPo3jx4gWORVtbG1OmTEFKSopC+ffff49Vq1ZhzZo1cHNzg6enJ9auXauQiLi5ucHU1BSVKlWShiJ6eXkhMzNT4YHExsbGOHz4MJo3b47SpUtj3LhxmDdvHpo1a1bgOAtiypQpGD9+PGbMmAEXFxc0bdoUf//9txRz37590b59e/j4+KBmzZqIj4/HgAED1BrDt04mhBDqaCglJQWamppf/KFu6pCYmAgTExMkJCRID7AjIiIi1aSkpCAmJgZOTk4K9xgVVFJSEpKTk3O9MhUbGwsDA4P/qQcIE9GXk9/5SpXcQOVhgXn5mJMmERERUV5yZivLzdcwFJCI6H0ffzclERERERERSZhcERERERERqQGTKyIiIiIiIjVgckVERERERKQGKk9okTMv//tkMhnkcjmcnZ3Rpk0bmJubf3JwRERERERE3wqVk6tz584hMjISmZmZKFOmDADgxo0b0NTURNmyZfHbb79hxIgROHr0qEpP9yYiIiIiIvqWqTwssE2bNmjUqBEePXqEs2fP4uzZs3jw4AEaN26Mrl274uHDh/Dw8MCwYcMKI14iIiIiIqKvksoPEbazs8PBgweVrkpduXIFTZo0wcOHDxEZGYkmTZrg2bNnag32c+FDhImIiD7dpz5EmIjoc1HXQ4RVvnKVkJCAuLg4pfKnT58iMTERAGBqaoq0tDRVmyYiIiIiIvpmfdSwwN69e2PXrl148OABHjx4gF27dsHf3x9t27YFAJw6dQqlS5dWd6xERET0H/ImMQUvHifkuu7F4wS8SUwplH79/Pwgk8mUlqZNm0p1HB0dpXIDAwNUqVIF27ZtU2gj5++id4WFhUEmk+Hly5cfjGPSpEmoVKmSUvmdO3cgk8lw/vx5qWzlypWoWLEiDA0NYWpqisqVK2PGjBmq7DYRqYHKE1osX74cw4YNQ5cuXZCRkZHdiJYWfH19sWDBAgBA2bJlsWrVKvVGSkRERP8ZbxJTsNRvNV7FJyFg0w8wK2oqrXvx6CV+6boCRhaG6L+2N/SM1T/ksGnTplizZo1Cma6ursLryZMno0+fPkhMTMS8efPg4+MDOzs71KlTR+3x5Gf16tUYOnQofvnlF3h6eiI1NRUXL17E5cuXP2scRPQRV64MDQ2xcuVKxMfH49y5czh37hzi4+OxYsUKGBgYAAAqVaqU6y8tRERERAWRkpyKV/FJiL/3HL90XYEXj14CeJtYxd97jlfxSUhJTi2U/nV1dWFjY6OwmJmZKdQxMjKCjY0NSpcujSVLlkBPTw9//fVXocSTnz179qBz587w9/eHs7MzXF1d0bVrV0ybNi3PbXKuoO3fvx+VK1eGnp4eGjRogLi4OOzbtw8uLi4wNjZGt27d8Pr1a2m7rKwszJgxA05OTtDT00PFihWxfft2aX1mZib8/f2l9WXKlMGiRYsU+s65qjd37lzY2trCwsICAwcORHp6uvoPDtFnpvKVqxyGhoaoUKGCOmMhIiIiAgCY2ZogYNMPUiL1S9cV6DHfB0HDtyD+3nNYFDfPvqJla/KlQwWQPYpHW1v7i9xzbmNjg/DwcNy9excODg4qbTtp0iT8+uuv0NfXR+fOndG5c2fo6upi48aNSEpKQrt27bB48WL8+OOPAIAZM2bgjz/+wLJly1CqVCkcPnwY3333HSwtLeHp6YmsrCwUK1YM27Ztg4WFBY4dO4YffvgBtra26Ny5s9RvaGgobG1tERoailu3bsHHxweVKlVCnz591HpsiD43lZOr5ORkzJw5EyEhIYiLi0NWVpbC+tu3b6stOCIiIvrvMitqqpBgLey4FADeJlbvDBVUt71798LQ0FCh7KeffsJPP/2kVDctLQ3z5s1DQkICGjRoUGgx5WXixIlo3749HB0dUbp0adSuXRvNmzdHx44doaGR/yClqVOnom7dugAAf39/jB07FtHR0ShRogQAoGPHjggNDcWPP/6I1NRUTJ8+HYcOHULt2rUBACVKlMDRo0exfPlyeHp6QltbG4GBgVL7Tk5OOH78OLZu3aqQXJmZmeHXX3+VnpPaokULhISEMLmib57KydX333+P8PBw9OjRA7a2tpDJZIURFxERERHMipqix3wfKbECgB7zfQo1sQKA+vXrY+nSpQpl5ubmCq9//PFHjBs3DikpKTA0NMTMmTPRokWLQo0rN7a2tjh+/DguX76Mw4cP49ixY/D19cWqVasQHBycb4L17igka2tr6OvrS4lVTtmpU6cAALdu3cLr16/RuHFjhTbS0tJQuXJl6fWSJUuwevVq3Lt3D2/evEFaWprS7SKurq7Q1NRU2IdLly591P4TfU1UTq727duHv//+W/qVg4iIiKiwvHj0EkHDtyiUBQ3fUuhXrgwMDODs7JxvnVGjRsHPzw+GhoawtrZW+MHZ2NgYd+/eVdrm5cuX0NTUlO5Tz4+xsTESEpRnS8yZadDERHFIZPny5VG+fHkMGDAA/fr1g7u7O8LDw1G/fv08+9DW1pb+LZPJFF7nlOWMUkpKSgIA/P3337Czs1OolzPZx+bNmzFy5EjMmzcPtWvXhpGREebMmYOTJ0/m2e/7/RB9y1Se0MLMzEzplxsiIiIidXt38gqL4uYYur0/LIqbK01y8aUUKVIEzs7OsLGxURrJU6ZMGVy5cgWpqYoTbkRGRsLJyUkpuchNmTJl8ODBA8TGxiq1IZfLUbx48Ty3LVeuHIDs2znUpVy5ctDV1cW9e/fg7OyssNjb2wMAIiIiUKdOHQwYMACVK1eGs7MzoqOj1RYD0ddO5eRqypQpmDBhgsLMMURERETq9OJxgkJiFbDpB5So6oCATT8oJlh5PAfrU6WmpuLJkycKy7Nnzwq8fffu3SGTydCzZ0+cPXsWt27dwurVq7Fw4UKMGDGiQG14e3ujTJky6Nq1K44dO4bbt29j+/btGDduHIYMGSINq+vfvz+mTJmCiIgI3L17FydOnEDPnj1haWkp3RulDkZGRhg5ciSGDRuGdevWITo6GpGRkVi8eDHWrVsHAChVqhTOnDmD/fv348aNGxg/fjxOnz6tthiIvnYqDwucN28eoqOjYW1tDUdHR6VfXiIjI9UWHBEREf03yQ10YWSRPaHEu0MA353kwsjCEHID3Xxa+XjBwcGwtbVVKCtTpgyuX79eoO1NTU1x5MgRjBkzBq1bt0ZCQgKcnZ0xf/58+Pv7F6gNLS0tHDhwAD/99BO6du2Kp0+fwsnJCUOGDMHw4cOleo0aNcLq1auxdOlSxMfHo0iRIqhduzZCQkJgYWFR8J0ugClTpsDS0hIzZszA7du3YWpqiipVqkgTffTt2xfnzp2Dz/+xd9fhUVxtG8DvjXtCIBAheAjFPXiAAsHKizvBXUpxD1asuBVogUBxdynuGsHdAoQESYh7nu8Pvp2yJFBSEjZy/65rL8iZM7vP7OzOzjPnzDlt2kClUqFdu3bo168fDh48mKpxEKVXKhGRlKzw8QgwyfHw8PimgNKD0NBQWFpaIiQkBBYWFtoOh4iIKEOKjo7GkydPkD9/fhgZpXyi36jQaERHxCQ73HrwqxAYmRqmyQTCRJT1fOl4lZLcIMUtV2mRPC1ZsgS//fYbAgICUKpUKSxatAgVK1b8bP2tW7di/PjxePr0KZycnDBz5kw0bNgw2bp9+vTB8uXLMW/ePAwePDjVYyciIqK0YWxh9NnkKb3Mb0VE9LEU33OV2jZv3owhQ4bAw8MD3t7eKFWqFNzc3PD69etk658/fx7t2rVD9+7d4ePjg6ZNm6Jp06a4efNmkro7d+7ExYsXYW9vn9abQURERJQixYoVg5mZWbKP9evXazs8IvoPvqpboLW1Ne7fv48cOXIgW7ZsX5zbKigoKEUBuLi4oEKFCli8eDEAIDExEY6Ojhg4cCBGjRqVpH6bNm0QERGBffv2KWWVKlVC6dKlsWzZMqXs5cuXcHFxweHDh9GoUSMMHjz4q1uu2C2QiIjo231rt8DM7tmzZ4iLi0t2Wa5cuWBubv6dIyLKur5rt8B58+YpX/D58+f/t4iTERsbCy8vL4wePVop09HRQZ06dXDhwoVk17lw4YLGTZzAh9F0du3apfydmJiITp06Yfjw4ShWrNi/xhETE6MxVGpoaGgKt4SIiIgoZfLmzavtEIgolX1VctW5c+dk//+t3r59i4SEBOTKlUujPFeuXJ8djScgICDZ+gEBAcrfM2fOhJ6eHgYNGvRVcUyfPv1fB+ogIiIiIiL6kq9KrlLSkqPtbnReXl5YsGABvL29v9h98WOjR4/WaA0LDQ1VJsMjIiIiIiL6Gl+VXFlZWf1roiIiUKlUSEhI+OoXz5EjB3R1dZPMPB4YGAhbW9tk17G1tf1i/TNnzuD169cas5YnJCRg6NChmD9/Pp4+fZrkOQ0NDWFomDbzZBARERERUdbwVcnViRMn0uTFDQwMUK5cORw7dgxNmzYF8OF+qWPHjmHAgAHJrqOeFO/jwSmOHDmizEDeqVMn1KlTR2MdNzc3dOrUCV27dk2T7SAiIiIiIvqq5MrV1TXNAhgyZAg6d+6M8uXLo2LFipg/fz4iIiKURMjd3R0ODg6YPn06AODnn3+Gq6sr5syZg0aNGmHTpk24evUqVqxYAQDInj17ktnI9fX1YWtrC2dn5zTbDiIiIiIiytq+Krm6fv36Vz9hyZIlUxRAmzZt8ObNG0yYMAEBAQEoXbo0Dh06pAxa4efnBx2df6bjqlKlCjZs2IBx48ZhzJgxcHJywq5du1C8ePEUvS4REREREVFq+qp5rnR0dKBSqfBvVVN6z1V6xXmuiIiIvt23znMVEhuLsLg45DY1TbLsRUQEzPX1YWlgkBqhaujSpQvWrFmTpNzNzQ2HDh0CAOTLlw/Pnj0DAJiYmMDZ2RmjR49Gq1atlOd4//69xlQxAHDy5EnUqlULwcHBsLKy+mIcEydOxK5du+Dr66tR/vTpU+TPnx8+Pj4oXbp0ss/p7+8PNzc3ZMuWDXv37oWlpWXK3wiiLOS7znP15MmT/x4pERERUQqFxMai/vGjeB0djZN13eD4UYL1PCICNY8cRk4jIxyqXSdNEqz69etj9erVGmWfDn41efJk9OzZE6GhoZgzZw7atGkDBwcHVKlSJdXjSYlHjx6hbt26KFq0KLZu3QpjY2OtxkOUlej8e5UPk9x97YOIiIjoW4XFxeF1dDQeh4ej5pHDeB4RAeCfxOpxeDheR0cjLC4uTV7f0NAQtra2Go9s2bJp1DE3N4etrS0KFy6MJUuWwNjYGHv37k2TeL7W9evXUa1aNVSuXBm7du36YmKlUqmwfPlyNG7cGCYmJvjhhx9w4cIFPHz4EDVr1oSpqSmqVKmCR48eaay3e/dulC1bFkZGRihQoAAmTZqE+Ph4ZfncuXNRokQJmJqawtHREf369UN4eLiy3NPTE1ZWVjh8+DB++OEHmJmZoX79+nj16lXqvyFE39lXJVefevToEQYOHIg6deqgTp06GDRoUJIvHhEREdF/ldvUFCfruqGAmZmSYJ1/81pJrAqYmeFkXbdkuwxqg56eHvT19REbG6u1GM6fPw9XV1e0aNEC69atg57ev3dQmjJlCtzd3eHr64siRYqgffv26N27N0aPHo2rV69CRDRGcD5z5gzc3d3x888/4/bt21i+fDk8PT3x66+/KnV0dHSwcOFC3Lp1C2vWrMHx48cxYsQIjdeNjIzE7Nmz8ddff+H06dPw8/PDsGHDUu/NINKSFCdXhw8fRtGiRXH58mWULFkSJUuWxKVLl1CsWDEcOXIkLWIkIiKiLMjxkwSr6uFDGomVYxomVvv27YOZmZnGY9q0acnWjY2NxfTp0xESEoLatWunWUz/plmzZvjpp5+wePHif52fVK1r165o3bo1ChcujJEjR+Lp06fo0KED3Nzc8MMPP+Dnn3/GyZMnlfqTJk3CqFGj0LlzZxQoUAB169bFlClTsHz5cqXO4MGDUatWLeTLlw+1a9fG1KlTsWXLFo3XjYuLw7Jly1C+fHmULVsWAwYMwLFjx1LlfSDSpq+65+pjo0aNwi+//IIZM2YkKR85ciTq1q2basERERFR1uZoaoq/qlZD1cOHlLK/qlZL08QKAGrVqoXff/9do8za2lrj75EjR2LcuHGIjo6GmZkZZsyYgUaNGqVpXF/yv//9Dzt37sSZM2dQvXr1r1rn41Ge1SM1lyhRQqMsOjoaoaGhsLCwwLVr13Du3DmNlqqEhARER0cjMjISJiYmOHr0KKZPn467d+8iNDQU8fHxGsuBD4OAFCxYUHkOOzs7vH79+pu2nyg9SHHL1Z07d9C9e/ck5d26dcPt27dTJSgiIiIi4MM9Vp3OndUo63TurHIPVloxNTVFoUKFNB6fJlfDhw+Hr68vXrx4geDgYIwcOVJZZmFhgZCQkCTP+/79e+jq6sL0K5LDLz0HgCQjAC5fvhxt27ZFgwYNcPr06a/ZTOjr6yv/V7d2JVeWmJgIAAgPD8ekSZPg6+urPG7cuIEHDx7AyMgIT58+RePGjVGyZEls374dXl5eWLJkCQBodJn8+DXUr/MVA1gTpXspTq5sbGySDAkKAL6+vsiZM2dqxERERESkMXhFATMznHOrr3EPVlonWP8mR44cKFSoEGxtbZN0w3N2dsatW7cQExOjUe7t7Y38+fMnSS6S4+zsjBcvXiAwMDDJcxgZGSFPnjwa5SqVCitWrECHDh3QsGFDnDp16j9u2eeVLVsW9+7dS5J4FipUCDo6OvDy8kJiYiLmzJmDSpUqoXDhwvD390/1OIjSqxR3C+zZsyd69eqFx48fK0ONnjt3DjNnzsSQIUNSPUAiIiLKel58klip77E6WddNKa955DBOpdGgFjExMQgICNAo09PTQ44cOb5q/Q4dOmDy5Mlwd3fHiBEjYGlpidOnT2P+/PmYNWvWVz2Hm5sbnJ2d0a5dO0ydOhW2trbw9vbGuHHj8PPPP0NXVzfJOiqVCsuWLYOuri4aNmyI/fv3o2bNml/1el9jwoQJaNy4MfLkyYOWLVtCR0cH165dw82bNzF16lQUKlQIcXFxWLRoEX766SecO3cOy5YtS7XXJ0rvUpxcjR8/Hubm5pgzZw5Gjx4NALC3t8fEiRMxaNCgVA+QiIiIsh5zfX3k/P+JPD8evOLjBCunkRHMv6IF6L84dOgQ7OzsNMqcnZ1x9+7dr1rfysoKZ86cwahRo9CkSROEhISgUKFCmDt3brK3VyRHT08Pf//9N8aMGYN27drhzZs3yJ8/P37++ecvXtBWqVRYsmQJdHR00KhRI+zbtw+1atX6qtf8N25ubti3bx8mT56MmTNnQl9fH0WKFEGPHj0AAKVKlcLcuXMxc+ZMjB49GjVq1MD06dPh7u6eKq9PlN6pJAUdXOPj47Fhwwa4ubkhV65cCAsLA/BhnofMJCWzMBMREVHyoqOj8eTJE+TPnx9G/58opURIbCzC4uKSbZl6EREBc339NJlAmIiyni8dr1KSG6Tonis9PT306dMH0dHRAD4kVZktsSIiIqL0wdLA4LNd/nKbmjKxIqJ0J8UDWlSsWBE+Pj5pEQsRERFRllGsWLEkc2mpH+vXr9d2eET0H6T4nqt+/fph6NChePHiBcqVK5dkKNGP50sgIiIiouQdOHAAcXFxyS5TzzlFRBlLipOrtm3bAoDG4BXquQlUKhUSEhJSLzoiIiKiTCpv3rzaDoGIUlmKk6snT56kRRxEREREREQZWoqTK15lISIiIiIiSirFA1oQERERERFRUkyuiIiIiIiIUgGTKyIiIiIiolTA5IqIiIgoBU6ePAmVSoX3799/19f19PSElZXVNz3H06dPoVKp4Ovr+9k62to+oszgPyVX79+/x59//onRo0cjKCgIAODt7Y2XL1+manBERERE35NKpfriY+LEidoOkYjSsRSPFnj9+nXUqVMHlpaWePr0KXr27Alra2vs2LEDfn5+WLt2bVrESURERJTmXr16pfx/8+bNmDBhAu7du6eUmZmZ4erVqyl+3tjYWBgYGKRKjESUfqW45WrIkCHo0qULHjx4ACMjI6W8YcOGOH36dKoGR0RERJlQRMTnH9HRX183Kurr6qaAra2t8rC0tIRKpdIoMzMzU+p6eXmhfPnyMDExQZUqVTSSsIkTJ6J06dL4888/kT9/fuWc6f379+jRowdsbGxgYWGB2rVr49q1a8p6165dQ61atWBubg4LCwuUK1cuSTJ3+PBh/PDDDzAzM0P9+vU1EsLExERMnjwZuXPnhqGhIUqXLo1Dhw59cZsPHDiAwoULw9jYGLVq1cLTp09T9J4R0T9SnFxduXIFvXv3TlLu4OCAgICAVAmKiIiIMjEzs88/WrTQrJsz5+frNmigWTdfvuTrpZGxY8dizpw5uHr1KvT09NCtWzeN5Q8fPsT27duxY8cO5R6nVq1a4fXr1zh48CC8vLxQtmxZ/Pjjj8ptFh06dEDu3Llx5coVeHl5YdSoUdDX11eeMzIyErNnz8Zff/2F06dPw8/PD8OGDVOWL1iwAHPmzMHs2bNx/fp1uLm5oUmTJnjw4EGy2/D8+XM0b94cP/30E3x9fdGjRw+MGjUqld8poqwjxd0CDQ0NERoamqT8/v37sLGxSZWgiIiIiNK7X3/9Fa6urgCAUaNGoVGjRoiOjlZaqWJjY7F27Vrl/Ojs2bO4fPkyXr9+DUNDQwDA7NmzsWvXLmzbtg29evWCn58fhg8fjiJFigAAnJycNF4zLi4Oy5YtQ8GCBQEAAwYMwOTJk5Xls2fPxsiRI9G2bVsAwMyZM3HixAnMnz8fS5YsSbINv//+OwoWLIg5c+YAAJydnXHjxg3MnDkz1d4noqwkxclVkyZNMHnyZGzZsgXAhxs//fz8MHLkSLT49GoTERER0afCwz+/TFdX8+/Xrz9fV+eTDjjfuTtbyZIllf/b2dkBAF6/fo08efIAAPLmzatx4fnatWsIDw9H9uzZNZ4nKioKjx49AvDh9osePXrgr7/+Qp06ddCqVSslkQIAExMTjb/t7Ozw+v/fo9DQUPj7+6Nq1aoaz1+1alWNrocfu3PnDlxcXDTKKleu/HVvABElkeLkas6cOWjZsiVy5syJqKgouLq6IiAgAJUrV8avv/6aFjESERFRZmJqqv26qeDj7noqlQrAh3ue/glHM57w8HDY2dnh5MmTSZ5LPcT6xIkT0b59e+zfvx8HDx6Eh4cHNm3ahGbNmiV5TfXrikhqbA4RpYIUJ1eWlpY4cuQIzp49i+vXryM8PBxly5ZFnTp10iI+IiIiokyhbNmyCAgIgJ6eHvLly/fZeoULF0bhwoXxyy+/oF27dli9erWSXH2JhYUF7O3tce7cOaW7IgCcO3cOFStWTHadH374AXv27NEou3jx4tdtEBElkeLkSq1atWqoVq1aasZCRERElGnVqVMHlStXRtOmTTFr1iwULlwY/v7+2L9/P5o1a4ZixYph+PDhaNmyJfLnz48XL17gypUrKbrtYvjw4fDw8EDBggVRunRprF69Gr6+vli/fn2y9fv06YM5c+Zg+PDh6NGjB7y8vODp6ZlKW0yU9aQ4uVq4cGGy5SqVCkZGRihUqBBq1KgB3U/7TBMRERFlYSqVCgcOHMDYsWPRtWtXvHnzBra2tqhRowZy5coFXV1dvHv3Du7u7ggMDESOHDnQvHlzTJo06atfY9CgQQgJCcHQoUPx+vVrFC1aFHv27EkyMIZanjx5sH37dvzyyy9YtGgRKlasiGnTpiUZ+ZCIvo5KUthRN3/+/Hjz5g0iIyORLVs2AEBwcDBMTExgZmaG169fo0CBAjhx4gQcHR3TJOi0FhoaCktLS4SEhMDCwkLb4RAREWVI0dHRePLkicY8T0RE6dGXjlcpyQ1SPM/VtGnTUKFCBTx48ADv3r3Du3fvcP/+fbi4uGDBggXw8/ODra0tfvnll5Q+NRERERERUYaV4m6B48aNw/bt2zWGAS1UqBBmz56NFi1a4PHjx5g1axaHZSciIiIioiwlxS1Xr169Qnx8fJLy+Ph4BAQEAADs7e0RFhb27dERERERERFlEClOrmrVqoXevXvDx8dHKfPx8UHfvn1Ru3ZtAMCNGzeQP3/+1IuSiIiIiIgonUtxcrVy5UpYW1ujXLlyMDQ0hKGhIcqXLw9ra2usXLkSAGBmZoY5c+akerBERESU8XCSWyJK71LrOJXie65sbW1x5MgR3L17F/fv3wcAODs7w9nZWalTq1atVAmOiIiIMi71tCyxsbEwNjbWcjRERJ8XGRkJANDX1/+m5/nPkwgXKVIERYoU+aYXJyIiosxLT08PJiYmePPmDfT19aGjk+IOM0REaUpEEBkZidevX8PKyuqb5+r9T8nVixcvsGfPHvj5+SE2NlZj2dy5c78pICIiIsocVCoV7Ozs8OTJEzx79kzb4RARfZaVlRVsbW2/+XlSnFwdO3YMTZo0QYECBXD37l0UL14cT58+hYigbNmy3xwQERERZR4GBgZwcnJKcjGWiCi90NfX/+YWK7UUJ1ejR4/GsGHDMGnSJJibm2P79u3ImTMnOnTogPr166dKUERERJR56OjowMjISNthEBGluRR3fr5z5w7c3d0BfOhLHRUVBTMzM0yePBkzZ85M9QCJiIiIiIgyghQnV6ampkrTvp2dHR49eqQse/v2bepFRkRERERElIGkuFtgpUqVcPbsWfzwww9o2LAhhg4dihs3bmDHjh2oVKlSWsRIRERERESU7qU4uZo7dy7Cw8MBAJMmTUJ4eDg2b94MJycnjhRIRERERERZVoqSq4SEBLx48QIlS5YE8KGL4LJly9IkMCIiIiIioowkRfdc6erqol69eggODk6reIiIiIiIiDKkFA9oUbx4cTx+/DgtYiEiIiIiIsqwUpxcTZ06FcOGDcO+ffvw6tUrhIaGajyIiIiIiIiyIpWISEpW0NH5Jx9TqVTK/0UEKpUKCQkJqRedloSGhsLS0hIhISGwsLDQdjhERERERKQlKckNUjxa4IkTJ/5zYERERERERJlVipMrV1fXtIiDiIiIiIgoQ0vxPVcAcObMGXTs2BFVqlTBy5cvAQB//fUXzp49m6rBERERERERZRQpTq62b98ONzc3GBsbw9vbGzExMQCAkJAQTJs2LdUDJCIiIiIiygj+02iBy5Ytwx9//AF9fX2lvGrVqvD29k7V4IiIiIiIiDKKFCdX9+7dQ40aNZKUW1pa4v3796kRExERERERUYaT4uTK1tYWDx8+TFJ+9uxZFChQIFWCIiIiIiIiymhSnFz17NkTP//8My5dugSVSgV/f3+sX78ew4YNQ9++fdMiRiIiIiIionQvxUOxjxo1ComJifjxxx8RGRmJGjVqwNDQEMOGDcPAgQPTIkYiIiIiIqJ0TyUi8l9WjI2NxcOHDxEeHo6iRYvCzMwstWPTmpTMwkxERERERJlXSnKDFHcLXLduHSIjI2FgYICiRYuiYsWKmSqxIiIiIiIi+i9SnFz98ssvyJkzJ9q3b48DBw4gISEhLeIiIiIiIiLKUFKcXL169QqbNm2CSqVC69atYWdnh/79++P8+fNpER8REREREVGG8J/vuQKAyMhI7Ny5Exs2bMDRo0eRO3duPHr0KDXj0wrec0VEREREREDKcoMUjxb4MRMTE7i5uSE4OBjPnj3DnTt3vuXpiIiIiIiIMqwUdwsEPrRYrV+/Hg0bNoSDgwPmz5+PZs2a4datW6kdHxERERERUYaQ4partm3bYt++fTAxMUHr1q0xfvx4VK5cOS1iIyIiIiIiyjBSnFzp6upiy5YtcHNzg66ursaymzdvonjx4qkWHBERERERUUaR4uRq/fr1Gn+HhYVh48aN+PPPP+Hl5cWh2YmIiIiIKEv6T/dcAcDp06fRuXNn2NnZYfbs2ahduzYuXryYmrERERERERFlGClquQoICICnpydWrlyJ0NBQtG7dGjExMdi1axeKFi2aVjESERERERGle1/dcvXTTz/B2dkZ169fx/z58+Hv749FixalZWxEREREREQZxlcnVwcPHkT37t0xadIkNGrUKMlgFt9iyZIlyJcvH4yMjODi4oLLly9/sf7WrVtRpEgRGBkZoUSJEjhw4ICyLC4uDiNHjkSJEiVgamoKe3t7uLu7w9/fP9XiJSIiIiIi+tRXJ1dnz55FWFgYypUrBxcXFyxevBhv37795gA2b96MIUOGwMPDA97e3ihVqhTc3Nzw+vXrZOufP38e7dq1Q/fu3eHj44OmTZuiadOmuHnzJoAPc3B5e3tj/Pjx8Pb2xo4dO3Dv3j00adLkm2MlIiIiIiL6HJWISEpWiIiIwObNm7Fq1SpcvnwZCQkJmDt3Lrp16wZzc/MUB+Di4oIKFSpg8eLFAIDExEQ4Ojpi4MCBGDVqVJL6bdq0QUREBPbt26eUVapUCaVLl8ayZcuSfY0rV66gYsWKePbsGfLkyfOvMYWGhsLS0hIhISGwsLBI8TYREREREVHmkJLcIMWjBZqamqJbt244e/Ysbty4gaFDh2LGjBnImTNniluHYmNj4eXlhTp16vwTkI4O6tSpgwsXLiS7zoULFzTqA4Cbm9tn6wNASEgIVCoVrKyskl0eExOD0NBQjQcREREREVFK/Oeh2AHA2dkZs2bNwosXL7Bx48YUr//27VskJCQgV65cGuW5cuVCQEBAsusEBASkqH50dDRGjhyJdu3afTbTnD59OiwtLZWHo6NjireFiIiIiIiytm9KrtR0dXXRtGlT7NmzJzWeLtXExcWhdevWEBH8/vvvn603evRohISEKI/nz59/xyiJiIiIiCgzSNE8V6ktR44c0NXVRWBgoEZ5YGAgbG1tk13H1tb2q+qrE6tnz57h+PHjX+wfaWhoCENDw/+4FURERERERKnUcvVfGRgYoFy5cjh27JhSlpiYiGPHjqFy5crJrlO5cmWN+gBw5MgRjfrqxOrBgwc4evQosmfPnjYbQERERERE9P+02nIFAEOGDEHnzp1Rvnx5VKxYEfPnz0dERAS6du0KAHB3d4eDgwOmT58OAPj555/h6uqKOXPmoFGjRti0aROuXr2KFStWAPiQWLVs2RLe3t7Yt28fEhISlPuxrK2tYWBgoJ0NJSIiIiKiTE3ryVWbNm3w5s0bTJgwAQEBAShdujQOHTqkDFrh5+cHHZ1/GtiqVKmCDRs2YNy4cRgzZgycnJywa9cuFC9eHADw8uVL5d6v0qVLa7zWiRMnULNmze+yXURERERElLWkeJ6rrIDzXBEREREREZDG81wRERERERFRUkyuiIiIiIiIUgGTKyIiIiIiolTA5IqIiIiIiCgVMLkiIiIiIiJKBUyuiIiIiIiIUgGTKyIiIiIiolTA5IqIiIiIiCgVMLkiIiIiIiJKBUyuiIiIiIiIUgGTKyIiIiIiolTA5IqIiIiIvpslS5YgX758MDIygouLCy5fvvzF+lu3bkWRIkVgZGSEEiVK4MCBAxrLd+zYgXr16iF79uxQqVTw9fXVWB4UFISBAwfC2dkZxsbGyJMnDwYNGoSQkBCljqenJ1QqVbKP169fp9q2U+bH5IqIiIiIvovNmzdjyJAh8PDwgLe3N0qVKgU3N7fPJjDnz59Hu3bt0L17d/j4+KBp06Zo2rQpbt68qdSJiIhAtWrVMHPmzGSfw9/fH/7+/pg9ezZu3rwJT09PHDp0CN27d1fqtGnTBq9evdJ4uLm5wdXVFTlz5kzdN4EyNZWIiLaDSG9CQ0NhaWmJkJAQWFhYaDscIiIiokzBxcUFFSpUwOLFiwEAiYmJcHR0xMCBAzFq1Kgk9du0aYOIiAjs27dPKatUqRJKly6NZcuWadR9+vQp8ufPDx8fH5QuXfqLcWzduhUdO3ZEREQE9PT0kix/8+YNHBwcsHLlSnTq1Ok/bCllJinJDdhyRURERERpLjY2Fl5eXqhTp45SpqOjgzp16uDChQvJrnPhwgWN+gDg5ub22fpfS32SnFxiBQBr166FiYkJWrZs+U2vQ1kPkysiIiIiSnNv375FQkICcuXKpVGeK1cuBAQEJLtOQEBAiup/bRxTpkxBr169Pltn5cqVaN++PYyNjf/z61DWlHy6TkRERESUyYSGhqJRo0YoWrQoJk6cmGydCxcu4M6dO/jrr7++b3CUKbDlioiIiIjSXI4cOaCrq4vAwECN8sDAQNja2ia7jq2tbYrqf0lYWBjq168Pc3Nz7Ny5E/r6+snW+/PPP1G6dGmUK1cuxa9BxOSKiIiIiNKcgYEBypUrh2PHjilliYmJOHbsGCpXrpzsOpUrV9aoDwBHjhz5bP3PCQ0NRb169WBgYIA9e/bAyMgo2Xrh4eHYsmWLxkiCRCnBboFERERE9F0MGTIEnTt3Rvny5VGxYkXMnz8fERER6Nq1KwDA3d0dDg4OmD59OgDg559/hqurK+bMmYNGjRph06ZNuHr1KhbOXYTgVyHIZmeJoKAg+Pn5wd/fHwDgddEH0eExyFcoL2xtbZXEKjIyEuvWrUNoaChCQ0MBADY2NtDV1VXi27x5M+Lj49GxY8fv/M5QZsHkioiIiIi+izZt2uDNmzeYMGECAgICULp0aRw6dEgZtMLPzw86Ov90rKpSpQo2bNiAcePGYcyYMXBycsLmDVtwetYl7H93DIM29sKev/coyRkA9OjbDQAwZtRY/Dp9Kry9vXHp0iUAQKFChTTiefLkCfLly6f8vXLlSjRv3hxWVlZp9A5QZsd5rpLBea6IiIiI0qfgVyFY2HY53vkFIXseawza2AvZ7K0Q7P8eC9ut+Kd8U29ks7PUdriUCXCeKyIiIiLKlLLZWWLQxl7Insca7/yCsLDdCjz2eqaZWG3sxcSKtIItV8lgyxURERFR+vZxS5Xaxy1ZRKmFLVdERERElKlls7dCp7ltNMo6zW3DxIq0iskVEREREWU4wf7v8deQzRplfw3ZjGD/99oJiAhMroiIiIgog/l08IrB2/pq3IPFBIu0hckVEREREWUYwa9CkgxeUaBc3iSDXAS/CtF2qJQFMbkiIiIiogzDyNQQ5tnNkgxekc3eSkmwzLObwcjUULuBUpbE0QKTwdECiYiIiNKvqNBoREfEJDvcevCrEBiZGsLYwkgLkVFmlJLcQO87xURERERElCqMLYw+mzxxfivSJnYLJCIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIgyhSVLliBfvnwwMjKCi4sLLl++/MX6W7duRZEiRWBkZIQSJUrgwIEDGstFBBMmTICdnR2MjY1Rp04dPHjwQKNOUFAQOnToAAsLC1hZWaF79+4IDw9Xlk+cOBEqlSrJw9TUNPU2nIiI0g0mV0RElOFt3rwZQ4YMgYeHB7y9vVGqVCm4ubnh9evXydY/f/482rVrh+7du8PHxwdNmzZF06ZNcfPmTaXOrFmzsHDhQixbtgyXLl2Cqakp3NzcEB0drdTp0KEDbt26hSNHjmDfvn04ffo0evXqpSwfNmwYXr16pfEoWrQoWrVqlXZvBhGRFqTHC1wfe/jwIczNzWFlZfVN2/mvhJIICQkRABISEqLtUIiI6CtUrFhR+vfvr/ydkJAg9vb2Mn369GTrt27dWho1aqRR5uLiIr179xYRkcTERLG1tZXffvtNWf7+/XsxNDSUjRs3iojI7du3BYBcuXJFqXPw4EFRqVTy8uXLZF/X19dXAMjp06f/24YSEaVDmzZtEgMDA1m1apXcunVLevbsKVZWVhIYGJhs/XPnzomurq7MmjVLbt++LePGjRN9fX25ceOGUmfGjBliaWkpu3btkmvXrkmTJk0kf/78EhUVpdSpX7++lCpVSi5evChnzpyRQoUKSbt27ZK8XmxsrJQvX14aNGgglpaWKd6+lOQGTK6SweSKPpWYmCjjx48XW1tbMTIykh9//FHu37//r+stXrxY8ubNK4aGhlKxYkW5dOmSxvKoqCjp16+fWFtbi6mpqTRv3lwCAgI06jx79kwaNmwoxsbGYmNjI8OGDZO4uDhl+fbt26VOnTqSI0cOMTc3l0qVKsmhQ4dSZ8MzsfS8T/39/aVdu3bi5OQkKpVKfv7551TZ5swqJiZGdHV1ZefOnRrl7u7u0qRJk2TXcXR0lHnz5mmUTZgwQUqWLCkiIo8ePRIA4uPjo1GnRo0aMmjQIBERWblypVhZWWksj4uLE11dXdmxY0eyrztgwAApXLjwV24ZEVHGkN4vcI0YMUI6duwoq1evTvPkit0CU5l8RRNmcv6tKTU6Ohr9+/dH9uzZYWZmhhYtWiAwMFCjjp+fHxo1agQTExPkzJkTw4cPR3x8vLL87NmzqFq1KrJnzw5jY2MUKVIE8+bNS50Nz+S+pnvQp76mm9Ivv/yCvXv3YuvWrTh16hT8/f3RvHlzZXlCQgIaNWqE2NhYnD9/HmvWrIGnpycmTJig1Dl9+jTq1q2LAwcOwMvLC7Vq1cJPP/0EHx+ftHkzMon0vE9jYmJgY2ODcePGoVSpUmnzBmQib9++RUJCAnLlyqVRnitXLgQEBCS7TkBAwBfrq//9tzo5c+bUWK6npwdra+tkXzc6Ohrr169H9+7dU7B1lJ5/Vz927tw56OnpoXTp0v95W4kyotjYWHh5eaFOnTpKmY6ODurUqYMLFy4ku86FCxc06gOAm5ubUv/JkycICAjQqGNpaQkXFxelzoULF2BlZYXy5csrderUqQMdHR1cunRJKTt+/Di2bt2KJUuWfPvGfo0Up25ZwLe0XH1NE+anvqYptU+fPuLo6CjHjh2Tq1evSqVKlaRKlSrK8vj4eClevLjUqVNHfHx85MCBA5IjRw4ZPXq0Usfb21s2bNggN2/elCdPnshff/0lJiYmsnz58hRvZ1byNVdPkvNvV3Hev38v+vr6snXrVqXOnTt3BIBcuHBBREQOHDggOjo6Gi0fv//+u1hYWEhMTMxnX7to0aIyadKklG9sFpGR9qmrqytbrv7Fy5cvBYCcP39eo3z48OFSsWLFZNfR19eXDRs2aJQtWbJEcubMKSIfuqwAEH9/f406rVq1ktatW4uIyK+//ppsK5SNjY0sXbo0SfmGDRtET08vSUsmfVl6/l1VCw4OlgIFCki9evWkVKlSqbr9ROldej4Gv337VhwdHeXUqVMiImy5ymhEBPPnz8e4cePwv//9DyVLlsTatWvh7++PXbt2fXa9uXPnomfPnujatSuKFi2KZcuWwcTEBKtWrQIAhISEYOXKlZg7dy5q166NcuXKYfXq1Th//jwuXrwIAPj7779x+/ZtrFu3DqVLl0aDBg0wZcoULFmyBLGxsQCAMmXKoF27dihWrBjy5cuHjh07ws3NDWfOnEnz9yYj+5qrJ5/6mqs4Xl5eiIuL06hTpEgR5MmTR+OqTIkSJTSunru5uSE0NBS3bt1K9rUTExMRFhYGa2vr/77RmVxG26f0ZTly5ICurm6SVofAwEDY2tomu46tre0X66v//bc6nw6YER8fj6CgoGRf988//0Tjxo2TtIbR56X331W1Pn36oH379qhcuXKavRdElHI9e/ZE+/btUaNGje/2mkyuUlFGO2Hz8fHB+fPn4erq+t83Ogv4mu5Bn/qabkoBAQEwMDBIMmrNp3WSe46P4/rU7NmzER4ejtatW3/F1mVNGW2f0pcZGBigXLlyOHbsmFKWmJiIY8eOffZkt3Llyhr1AeDIkSOoXLky4kMjYGdkDltbW406oaGhuHTpEiqWKqM8x/v37+Hl5aXUOX78OBITE+Hi4qLx3E+ePMGJEyfYJTCFMsLv6urVq/H48WN4eHikzkYTZTDp+QLX8ePHMXv2bOjp6UFPTw/du3dHSEgI9PT0lIstqY3JVSrKKCdsuXPnhqGhIcqXL4/+/fujR48eKdjKzG/9+vUwMzNTHnFxcdoO6att2LABkyZNwpYtW5LcC5KVZeR9Sl9nyJAh+OOPP7BmzRrcuXMHffv2RUREBLp27QoAcHd3x+jRo5X6P//8Mw4dOoQ5c+bg7t27mDhxIq5evYo+XbrBp8sE+LQbjf7demDq1KnYs2cPbty4gQ6t2yC7ygB5d/sgPjQCP/zwA+rXr4+ePXvi8uXLOHfuHAYMGIC2bdvC3t5eI75Vq1bBzs4ODRo0+K7vS0aX3n9XHzx4gFGjRmHdunXQ09P7D1tIlPGl5gWuchUr4kVEBPLnz5/kAtedV69w6dIl5Tm/5gLXhQsX4OvrqzwmT54Mc3Nz+Pr6olmzZqn2HnyMR4JvsH79evTu3Vv5e//+/VqM5uudOXMG4eHhuHjxIkaNGoVChQqhXbt22g4r3WjSpInGVeeYmBgAH66W2NnZKeWBgYGfvXH5a67i2NraIjY2Fu/fv9f4gf+0zqc3Yauf89OrQZs2bUKPHj2wdevWJDeJZnUZdZ/S12vTpg3evHmDCRMmICAgAKVLl8ahQ4eUk2E/Pz/o6PxzPbFKlSrYsGEDxo0bhzFjxsDJyQm7du1CkbwF4P0uBFF+Aah1RhDZpRt69eqF9+/fo4RxdkzLXhY67yMQHxEFPQtTrF+/HgMGDMCPP/4IHR0dtGjRAgsXLtSILTExEZ6enujSpQt0dXW/6/uS0WSk39WEhAS0b98ekyZNQuHChbUdDpFWDRkyBJ07d0b58uVRsWJFzJ8/P8kFLgcHB0yfPh3Ahwtcrq6umDNnDho1aoRNmzbh6tWriHDvCNcjh3GyrhsGDx6MqVOnwsnJCca5cqHpoIFANivUatgQADQucC1btgxxcXFJLnD98MMPGnFevXoVOjo6KF68eNq9GSm+oysL+Nqb1kJDQ+XBgwfK4+bNm/86dO+nvmYI4WPHjgkACQ4O1qiTJ08emTt3roiIjB8/PslNtI8fPxYA4u3t/dltmDJlCocF/hfqwQ9mz56tlIWEhHzV4AcDBgxQ/g6KihLbZAY/2LZtmzwPD5f3MTFy9+7dZAc/+Pgm7OXLl4uFhYVER0crZRs2bBAjIyPZtWtXqm13ZpZa+zQhKkQc7O2S3acSFiASE/af96kaB7T4/qJevpZzNbrL0XyN5FyN7hJ89bbG31EvX2s7xEwrI/2uBgcHCwDR1dVVHiqVSik7duzYf34fiDKiRYsWSZ48ecTAwEAqVqwoFy9eVJa5urpK586dNepv2bJFChcuLAYGBlKsWDFZs327FNi5XfDXGimwc7s8CwuT8ePHi03OnKLS1xcUKyq5Fy+S5+HhynO8e/dO2rVrJ2ZmZmJhYSFdu3aVsLCwz8b4PQa0SBfJ1b/NG/OpLVu2iLOzsxgaGkrx4sVl//79Gsv/6/w1av91tMDUOmELD44QO7vkT8KD/N9LZEjUN5+wqU2aNEny5s2bou3MimbMmCFWVlaye/duuX79uvzvf/9LMlpV7dq1ZdGiRcrfmzZtEkNDQ/H09JRLvr6Ss0F90TE1Fa9Hj5Q6ffr0EQdHR7GbPFGKL5wvFStVksqVKyvL1aNV1atXT3x9feXQoUNiY2OjMVrV+vXrRU9PT5YsWSKvXr1SHu/fv0/jdyVj+9Z9etv3ivSqV1isTPQl4NF1pU6fPn0kj6ODHB9bRa7ObiyVK1VM8T4VEfHx8REfHx8pV66ctG/fXnx8fOTWrVtp+I7Qxz5OsNQPJlbfX6pdCElIEAcHh+QvhPy/lP6uJiQkyI0bNzQeffv2FWdnZ7lx44aEf3QCSERfxy88XCPBOvc6UONvPy19rzJUcqWtGZ2/5FuHYv+WEzbvyz5SKV9VMdIzlru+95Q6ffr0kdwOuaVzyV4yuOZIcanokuITtsWLF8uePXvk/v37cv/+ffnzzz/F3Nxcxo4dm+LtzGrUCXuuXLnE0NBQfvzxR7l3755Gnbx584qHh4dG2cdXcQydnAQeEzQODvffvhWL+vUFpqaiMjSU+j/9JK9evdJ4jqdPn0qDBg3E2NhYcuTIIUOHDtWYcNbV1VUAJHl8eoWINKXGPq1Y0EoujioisqGJSNiH/Rb15on0q5tPspnoiomhrjT7qWGK96mIJLtPeSHk+wq+elsjuQq+elvbIWVJ33wh5PZt6dq1q1haWmoMg9+nTx/JkyePbNu2TU6fPi2VK1f+TxdCPubh4cGh2Im+0ccJlvqhzcRKJIMlV9qY0fnffEtylRonbA7mjtLKrpNMrDFTgl4Gi4iI/6NXUsGushjqGIm+jr40btA4xSdsCxculGLFiomJiYlYWFhImTJlZOnSpZKQkJDi7aSUS69XY+gbhL36kFgtL/fh31e+mn+Hvfr356B0iS1X6Udq/K7myJFDqlevrvG7GRUVJV26dBF9fX3R09OTn/7Dxa1PMbkiSh3nXgdqJFfnXiff6PK9pCQ3UImIpN0dXV8WGxsLExMTbNu2DU2bNlXKO3fujPfv32P37t1J1smTJw+GDBmCwYMHK2UeHh7YtWsXrl27hsePH6NgwYLw8fHRuDHd1dUVpUuXxoIFC5I8Z0xMjHKDO/BhuF1HR0eEhITAwsIiVbY1JYL932NhuxV45xeE7Hms0WluG/w1ZLPy96CNvZDN3uq7x0Xf7nlEBGoeOYzH4eFKWQEzM5ys6wZHU1MtRkb/WXgAsLc3EPbynzJzB+Cn5YAZB6jIiKL938C73WhE+QXAOI8tis4dittD5ih/l904HUb2NtoOk75SYGAgevXqhZcvX8LBwQHLly+Hra0tAgIC0Lt3b6V8xYoVnIOMKB1Ij+dKoaGhsLS0/KrcQKtDsX/NcKmf+tzQqB8Pnaou+9rnnD59OiwtLZWHo6Pjf9qe1JLN3gqDNvZC9jzWeOcXhPktf2dilUk4mprir6rVNMr+qlqNiVVGZmYL1JqsWVZrMhOrDCr61VuNxKrsxumwKvcDym6cDuM8tojyC4B3u9GIfvVW26HSV8qVKxeWL18OBwcHvHz5Er1798a1a9c0Eqvly5czsSJKBz5OrAqYmeGcW30UMDPD4/Bw1DxyGM8jIrQd4r/iPFcARo8ejZCQEOXx/PlzbYeEbPZW6DS3jUZZp7ltmFhlcM8jItDp3FmNsk7nzmaIgwV9RngAcGKCZtmJCR/KKcPRMzWGfnbLJC1URvY2SoKln90SeqbGWo6UUsLW1lYjwerevXuSliwi0q4XnyRWJ+u6oYpNTpys66aRYL1I5+dMWk2utDWj86cMDQ1hYWGh8dC2YP/3+GvIZo2yv4ZsRrD/e+0ERN8sM1yNoU983CXQ3AFosvLDv2EvP5Qzwcpw9CxMUcZzMspumpGk65+RvQ3KbpqBMp6ToWfB1uaMxtbWFpMna7YyT548mYkVUTphrq+PnEZGSboAOpqaKglWTiMjmOvraznSL9NqcpWaMzqr6yc3o3NoaKjGjM7p3af3XA3e1lfpIriw3QomWBlQZrkaQx8JD9RMrH5aDtiW+vCvRoIV+O/PRemKnoUpjOxyJLvMyC4HE6sMKiAgABMmaLYyqyedJiLtszQwwKHadXAqmXurHE1NcaquGw7VrgNLAwMtRfh1tN4tcMiQIfjjjz+wZs0a3LlzB3379k0yo/Po0aOV+j///DMOHTqEOXPm4O7du5g4cSKuXr2KAQMGAABUKpUyo/OePXtw48YNuLu7w97eXmPQjPQq+FWIRmI1aGMvFCiXV+MerIXtViD4VYi2Q6UUyCxXY+gjBqaAsXXSwSvMbP9JsIytP9QjIq36dPCKlStXatyDxQSLKH2wNDBA7s/ch57b1DTdJ1YAoKftANq0aYM3b94oV49Kly6NQ4cOKTeW+vn5QUfnnxywSpUq2LBhA8aNG4cxY8bAyckJu3btQvHixZU6I0aMQEREBHr16oX379+jWrVqOHToEIyMjL779qWUkakhzLObAYDG4BXqQS4WtlsB8+xmMDI11GKUlFLqqzFhcXFJDhrqqzHm+voZ4qBB/8/ADGi4CIiNAMw+uRHezBb4acWHxMrATDvxERGAD7cFfDp4hfoeLHV57969OVogEaUKrQ7Fnl6lZLjFtBAVGo3oiBhks7NMsiz4VQiMTA1hbJH+E0UiIiJtCw8Px8CBAxEUFJRk8Ap1i5a1tTUWLVoEMzNeDCGipFKSGzC5Soa2kysiIiJKPeHh4YiIiEi2ZSowMBCmpqZMrIjos1KSG2i9WyARERFRWjIzM/ts8sSugESUmrQ+oAUREREREVFmwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUwOSKiIiIiIgoFTC5IiIiIiIiSgVMroiIiIiIiFIBkysiIiIiIqJUoKftANIjEQEAhIaGajkSIiIiIiLSJnVOoM4RvoTJVTLCwsIAAI6OjlqOhIiIiIiI0oOwsDBYWlp+sY5KviYFy2ISExPh7+8Pc3NzqFQqrcYSGhoKR0dHPH/+HBYWFlqNhVIP92vmw32aOXG/Zj7cp5kP92nmlJ72q4ggLCwM9vb20NH58l1VbLlKho6ODnLnzq3tMDRYWFho/YNFqY/7NfPhPs2cuF8zH+7TzIf7NHNKL/v131qs1DigBRERERERUSpgckVERERERJQKmFylc4aGhvDw8IChoaG2Q6FUxP2a+XCfZk7cr5kP92nmw32aOWXU/coBLYiIiIiIiFIBW66IiIiIiIhSAZMrIiIiIiKiVMDkioiIiIiIKBUwuSIiIiIiIkoFTK6IiIiIiIhSAZMrItKqxMTEJGUcxJTo2/A7lHVx3xNpF5Mr+mbqA/mrV68QEhKi5WgoI0lMTISOzofD0MaNG7Fp0yYAgEql0mZYWqX+Pt27dw9v377VcjSUUag/N69fvwaQtb9DWZmIQKVS4ciRI5gwYQKioqK0HVK6Fhsbq+0QKJVt3LgRc+bM0epFBiZX9E3UB/I9e/bA3d0de/fuRWRkpLbDogxARJTEasSIERg1ahRCQ0Ph7++vUScrUX+fdu/ejXr16mHz5s0ICwvTdliUzqk/N/v27UPv3r2xfft2bYdEWqJSqbB9+3a0bdsW7969w5MnT7QdUrp17NgxTJ8+Hffv39d2KJQKRASRkZHw8PBAtmzZtHqBickVfROVSoVdu3ahbdu2qFevHmrUqAETExNleVY7Oaavpz7wzZs3D2vXrsXmzZvRq1cv2Nvba9TJSp8h9Qly+/btMXLkSDRp0gTm5ubaDovSOZVKhR07dqBVq1aoWrUqihcvrrE8K32HsrrLly+jR48e+O2337BkyRIULVoUABAXF6flyNIXT09PdO3aFUFBQXjx4oW2w6FUYmxsjMTERJiammo1Dj2tvjpleM+ePcO4ceMwd+5c9OnTB3FxcQgPD8eVK1fg6OiIQoUKaXT9IlJ/HhISEhAbG4tTp05hwIABqFSpEh49egQfHx94enpCX18fK1asgI2NjXJlPrOLiIjAwoULMWTIEPTr1w8xMTEICAjArl274OTkhDJlysDa2lrbYVI68/DhQ4waNQqLFi1Cjx49kJiYiISEBFy5cgVlypSBoaEhj8NZxK1bt1C5cmV069YNwcHBOHbsGNatWwd/f3906dIFXbp00bgAmhWtX78eAwcOxPLly9G4cWNYWFhoLM8qvzeZkUqlgp6eHoyNjQFob18yuaIU+/jDqqenBz09PdjZ2SEyMhLz5s3DwYMH8fDhQ8TFxWHv3r2oUqWKliOm9ER9ghcbGwtjY2OYm5vj6NGjyJ49O3bs2AEdHR3kypUL169fx//+9z+cP38+U//Qqb9P8fHx0NXVxatXr5A9e3Y8f/4cixYtwpUrV+Dt7Y08efKgc+fOGDJkCE+SSeM4HBERARGBi4sLEhMTsWDBAmzfvh13796FjY0Nrly5AjMzMy1HTGkpICAAtra20NfXx6FDh7Bq1SqsXbsWZmZmyJkzJ6ytrTFp0iTUr18fBQoU0Ha4WhMYGIgVK1Zgzpw5aN++vVIeGhqKx48fQ0dHB8WKFYOurq4Wo6SU2LZtGwwMDNCkSRMEBQXh/fv3So8PbZ078BeaUkylUuH8+fM4ceIErKyskC1bNixcuBC5c+fG1atX0axZMxw+fBhFihTBwYMHtR0upUMbNmxAlSpVEBMTg9atW8PGxgbjx49HjRo1MHXqVKxduxY9evRAzpw5kZCQoO1w05RKpcK5c+ewfv16GBkZoV69ehg7dixKliyJR48eoWPHjggJCUHhwoXh7e3NxIoA/PO5CQoKgqOjI3LlyoUBAwagQIECOHnyJGrXro2jR48iJCQEixYt0na4lEZEBHfu3NE4XgwePBgzZsyAs7Mzxo8fj1WrVmHRokXImTMngoKCtB2yVkVFReH+/fuwtbVVylasWIGuXbvCxcUFLi4uGDRoEN6/f6+9IOmrxcfH4++//0bTpk1x4MABZMuWDZGRkTAwMNBqXGy5ohQLCwvDsmXLEBMTg1q1auG3336Dt7c3mjVrhvbt2yvdliwsLJI0t1PWpO6SpL7ani1bNpiZmWHPnj1o1aoVfvzxR4SFhSFXrlzKOnv27IG9vX2WuIK4bt067NixA+3atcOcOXNQu3ZtJCYmokGDBkoddffIuLg46OnpZerWPPp34eHhGD9+POzt7bFu3TpMmDABly5dgr6+Pjp27IjcuXMDAIoVKwYHBwctR0up6eNWS5VKhR9++AGFCxdGnz59cOjQIcydOxdjxoxBjhw5lHWmTp2KxMRE5MmTR1tha1VcXBz09fWhr6+PwoUL4++//0auXLkwb9483LlzB1WrVsXevXsRHh6Oli1bokqVKujQoYO2w6YviI+Ph56eHmbNmgVjY2M0bdoUq1evRsmSJbFt2za8fPlSGcHawsICMTExuH//Pjp27IgiRYqkbXBC9BUSExM1/l69erU4OzvL33//LSIicXFxyrLw8HAZO3as5MqVS+7fv/9d46T05+PPzqtXr0REJCoqStzd3aVGjRoSExOjLA8LC5Pjx49LnTp1pESJEsrn6tPPX0an3p6EhASlrGzZstK2bdskdZ8/fy5jx44VS0tLuXnz5neLkdKXxMREje9BfHy8LFmyRGrWrCkXL15U6qhFRUXJhAkTxM7OTh4+fPjd46W0o97P/v7+yv/Pnz8vlStXlt9//12j7v79+6VXr16SPXt28fb2/u6xpge7du2Sxo0bS0hIiIiIzJo1S8qUKSM2NjZSsmRJOXLkiLx7906pX6ZMGZkwYYK2wqWvsGvXLmnUqJGyT9+/fy/9+/cXlUolKpVKypcvL05OTuLg4CC5c+eWwoULS8GCBaVy5coSHx+f5vExuaKvdu7cOdm4caPyd7du3SRv3rwSHR2tlK1bt07c3d0ld+7cWfZATsmbOnWq5MqVS7Zt2yYiHw6Gjo6OMmjQIKXOsWPHpHv37tKqVSslsfo4cc9M/v77b5k5c6b4+vqKyIcfi6pVqyrvj4jI2bNnpX79+lK4cGHx8fHRUqSUnly6dEnOnTsnIh8SKFdXV2nQoIFGnR07dki3bt3E1taWx+FM6vLly6JSqWTYsGFy/PhxEREZNGiQuLm5SXBwsIh8uJi1ZMkSadiwYZa+MHPx4kUxMjKSli1bKhfznj9/Lrdu3UpS99WrV1KhQgVZu3bt9w6TUkC9T1u1aqUkWG/fvpXx48eLSqWSrVu3iohIbGysxMXFSXR0tMTExCR7YTMtMLmif5WYmCivX79Wrgh0795dXr16Jc+ePZP69evLwIEDlSsBV69elV9//VUePHig5agpvRk4cKCoVCrJlSuXjBgxQvbv3y979+6VSpUqye7du5V69+/fVw6AmTWxCg0NlTp16ohKpZLGjRvL3LlzJTExUZo1ayadO3dWvk+hoaGyZ88eefLkiXYDJq1LTEyUhw8fKsfh33//Xd6+fSt+fn7i6Ogo06dPV+ru3r1bPDw85N69e1qMmNLSvn37xMzMTNzc3KR169YyduxYefPmjWTPnl0mTZqk1AsNDZXQ0FAtRpo+XLlyRbJnzy5NmzZN9v1ITEyU4OBgadSokVStWvW7tG7Qt1Hv0+bNmyv79N27d9K/f38xMDCQXbt2iciHffvxuURaJ1YiIioRToBBX2fGjBk4fPgwXr58iTJlyqBo0aKIiIhAQEAAevfujapVqwL4px8s0cfev3+PcePGISoqCtbW1nj48CFev34NOzs7ODo6YurUqRpzU0gmGw730+1ZtWoVpk+fjuHDhyv3WRUvXhwDBw7E6tWr0blzZy1GS+lV7969sXPnThQsWBAlS5ZEwYIFYWxsjBMnTmDUqFGoWLEigA+jcWr7pm5KfcHBwciWLRsAoF+/fjhw4AA2bdqE/v37w9nZGYmJiTh69Cg2b96MH3/8UcvRaldCQoLGPbtXrlxBgwYNULNmTaxcuRKWlpYAPrynO3fuxObNm/H27VtcvHgR+vr6SdYn7fvcPq1RowY8PT1hYWGB4OBgeHh44I8//sD69evRvHnz7x4nh52iL7p586by//r166Nw4cKYNWsWGjZsiMDAQKxevRpbtmzBmjVrlHpMrEht6tSpGDVqFHx9fWFlZYXixYvD0NAQPXr0wLRp02BmZoYdO3ZgwYIF8PHx0Vg3MyVWwIft8fb2xpYtWwAA3bp1g7OzM06ePImbN28iJiYG169fh4mJCYYOHYrr169rOWJKL549e4bo6GgAQN++fdGmTRu0bt0a5cqVw5EjR+Dh4YGbN2/iyJEjyjpMrDKfq1evolu3bli4cCEAYNGiRShVqhSuXLkCLy8v5MuXD2FhYQgKCsK2bduy7MTBp0+fRkBAAHR1dTVGm61QoQIOHTqEkydPol+/fkr5vn37cPz4cRQpUkQZFEY9NQalD/+2T0+fPo0+ffoAALJly4aJEyeiZcuWWLBggXYCTvO2McqwgoKCJHv27OLi4iI7duyQhIQEmTt3rpQrV07i4+MlLi5OFi9eLLq6umJiYiLv3r3LdAMP0LdZtWqVFClSROrXry/z58+XuLg4qVixoowePVqps2DBAuncuXOm7QIo8k+Xk+7du4u1tbX069dPwsLC5NmzZ9KwYUPZvHmzJCQkyN9//y2tW7cWY2Njef78ubbDpnTg5cuXolKppFu3brJ//34RERk5cqT06dNHREQiIyOlR48eolKpxN7eXsLDw7UZLqWhc+fOSb9+/cTBwUFatmwpvr6+snTpUunXr5+8ePFCRESuXbsmo0aNktu3b2s5Wu14/vy5VKxYUapXry6BgYEiIkm6+J06dUoMDQ1l6tSpStnLly+V8xd2CUxfUrJPp0yZopSFhYV9ly6AyWG3QPqiV69eYfDgwfD394e9vT2WL1+Ozp07w87ODsuWLQMAXLp0Cfb29nB0dNRytKRN6uHW1dRD3z58+BBbt27FwoULUbNmTdSsWRMeHh5YuXIlGjVqpPEcmb1L6bt373D16lV06dIFJUqUQM2aNZGQkIDo6GiMHDlSmbrg7du3GsMoU9a2b98+rFmzBk+ePEGdOnUwaNAgVKhQAUOGDMHQoUMBAPv370fRokWRP39+LUdLqUX+vytxVFQU9PX1lWOjl5cX3N3dkTt3btja2sLHxwc9e/bEwIEDtRyx9okINm7ciD///BP6+vpYu3YtcuXKlaQ72ahRo+Dt7Y3t27crE86q189svSYyupTu0x07dmhMmv7pucn3wOSKFOqDyqNHjxAbG4vQ0FC4uLggPj4eBw4cwJIlS+Dl5YUGDRrg4cOHmDZtGmrVqqXtsCkd+PjgtXjxYty6dQvXr1/HgAEDULNmTdjZ2cHPzw8dOnRAXFwc7ty5Azc3NyxZsgQ2NjZajj5tqL9P169fx61bt6Crq4tixYqhWLFiCAgIwK+//oqHDx/i4sWL0NXVxcyZM9G9e3dth01apv7c+Pv7IzY2FkZGRrC1tUVQUBCOHDmCIUOGoGTJksiRIweuX7+OP/74Q7nPijIP9efg4MGDWLduHe7fvw8XFxc0btwY9evXR3R0NH777Tfcvn0bmzdvBgCcOHECrq6uWo5cez5OjLZu3YpFixbByMgIf/31F3LlyqVx8c7DwwNeXl7Yu3cvk6l0LMPuU200l1H6o24O37FjhxQqVEhKlSol5ubm0q5dO2WoaBGRGTNmiLOzs6hUKunfv7/WmlwpfRoxYoTkypVLJk6cKCNHjhQrKyvp1auXBAUFKXWWLVsm5cuXF1dX10zfjXTbtm3i4OAgLi4uUrt2bTE3N1eGWo+KipKLFy9Kr169RKVSScGCBSUyMjLTvyf0eep9v3PnTilatKgUK1ZM7OzsZOTIkcrIf9HR0dK5c2cpV66cqFQqmTx5MrsxZVK7du0SIyMjmTJliixZskRat24tenp6yrDq8fHxcu/ePRkyZIhYWVnJ48ePtRyx9qm/Q4mJibJlyxapXr261K1bV/z9/ZU6kZGR0rBhQxk6dKi2wqQUyIj7lMkVKU6ePCnm5ubyxx9/SGJiouzdu1dUKpWsX79eo96FCxdk9OjRWbZPN2lSH/hOnjwpBQoUkKtXr4rIP/OwqD8/H99T5e/vryTmmTWZ8PLykuzZs8uyZctE5J/3Y8SIEclOys0Jt0lE5OjRo2JmZiYLFiyQqKgomTp1qujo6MiWLVs0kqjDhw9L9+7d5c6dO1qMllKb+rgYHBwsderUkXnz5omIyOvXr8Xe3l4GDBiQ7HofX8DK6j49GXd1dZUffvhBDh48KJs3b5amTZtKqVKlMu0k9ZlRRtunTK6yoE9bm9QfwokTJ0rnzp1F5MNcQ05OTtKjRw+lnnryPRHe8Ekfbhb92OHDh6V69eoiIrJx40YxMzOTpUuXKnUvXrwoUVFRGutkxpZP9Xdj06ZN0rRpUxERefr0qTg6Okq/fv2Uei9fvtRKfJQ+fPrjr/7c9O/fXxms4vnz51KoUCHp3bu3Uu/j7x2Pwxmf+nPw/v17jfK3b99KwYIFxcfHR16+fCkODg7Ss2dPZfm2bdvk4cOHSZ6HPvj4/Thz5oy0bNlSLCwspEqVKtKhQwflJJzfoYwjI+1TDsWeBeno6MDPzw+PHz8G8M+Q13fu3EH+/PkhIqhVqxZq1aqFFStWAABWr16N/fv3K8/BIUqztl27dqFXr164d++eUhYSEoI3b95gz5496NOnD2bOnIm+ffsCAI4dO4alS5fi3bt3Gs/zvW8yTSuPHz/G0qVLAfzz3QgNDUVERATu3LmD6tWro0GDBli0aBEA4OjRo5g9ezaCg4O1FjNpl0qlQkBAAB49egTgn8/NmzdvUK1aNURFRcHFxQW1a9fG77//DuDDPQdXrlxRnoPH4YxPpVLh9evXaNasGebOnauU6+jo4IcffoCXlxeqVq2Khg0bKp+DFy9e4MCBA7h165bG82RF8plhA1QqlbKsWrVq2Lp1K65du4ZDhw7hr7/+gp6eHuexSqcywz7NHGc2lCKJiYlo2bIlGjZsiIcPHyrl9erVw7p162BjY4NmzZph6dKlygH79OnTOHr0KGJiYrQVNqUjZmZm2L59O3777Tfcv38fANC0aVPY2NigadOmmDRpkjKPSHR0NP7880/ExcXB3t5em2GniYSEBGzduhVDhw7FvHnzlPICBQogKCgIrq6uqFevHpYvX64kk3v37kVAQECmHhmRPk9EEBUVherVq2P48OF48OCBssze3h5TpkyBs7MzWrRogcWLF0OlUiEuLg47duzAiRMnEB8fr8XoKbWFhYUhd+7c2LRpk5JAZcuWDQ4ODujZsyfKli2L5cuXKyeNS5YswaVLl1CmTBlthq0127Ztw+DBgwFonnB/Sn3+ol6eN29emJubQ6VSITExMV2chNMHmW6faqvJjLTr1atXUrRoUXFxcVFulL5796789NNPUqBAAbl06ZKIiISHh8uYMWPEzs5O7t69q82QScsSEhIkMTFRaXI/ceKEZMuWTbp06aJ8hvbu3SvlypWTypUry7Fjx2Tt2rVSv359KVasWLrpC50Wnj17JpMmTRIHBwf57bfflPKePXuKSqWSNWvWSEBAgLx69UpGjhwpOXLkkFu3bmkxYkoPDh8+LLly5ZLOnTsr97A+ePBAqlWrJrlz55aIiAgR+dDNZcyYMeLo6Mh78zKp+/fvS9++faVMmTKyaNEipbx58+ZiY2Mj06dPl99++0169+4t5ubmGgNNZTWrV68WlUolI0eOVMr+7XclM/7uZCaZbZ8yucoCPr2vRX2SGxgYKE5OTuLi4qL03d67d6/Uq1dPsmfPLjVq1BBXV1exs7MTb2/v7x43pS+f3i8lInLkyBHJli2bdOrUSZ4+fSoJCQly+PBhqVu3ruTIkUMqVaok7dq1k9jYWBFJH32h08qzZ89kwoQJ4uDgIDNmzFDKmzVrJs7Ozkrf8AIFCvD7lAV9fByOj49X/j5+/LhYW1uLu7u7chzeuHGjlCxZUvLmzSstW7aUhg0bio2NDT83mYB6v0dFRSX5bb5165b07dtXSpcuLQsXLlTK+/XrJzVr1pTSpUtLx44d5caNG9815vQmLi5O1q9fLwYGBjJs2DCl/HMn2x+XP3jwgBNtp0OZbZ8yucrk1B/AZ8+eyeHDh5Oc3AYGBkrBggWlXLlyyjCu9+7dkzVr1sjgwYNlxYoV8ujRo+8eN6Uv69atkyJFisi8efNky5YtIvJPsnXs2DGxtraWDh06yJMnT5R1njx5ojG0+MejBWZ0Dx8+lNWrV8v58+c1yl+8eCHjxo0Te3t7+fXXX5Xyc+fOyYYNG+T06dMcyCILUp9E+/n5yf3795WLDWp///238h169uyZiHw4Do8YMUJ69uwp06ZNkwcPHnz3uClt3Lp1SwoWLCgtW7aUmTNnyvXr15Xj44sXL6RPnz5SpkwZmT9/vrLO+/fvJSoqSmNgqazm4/OXuLg4+euvv5KcjH9uwC4RkYULF0qFChXk+fPnaR8sfZXMuk+ZXGUB/v7+YmVlJSqVSpo0aSLNmzeXI0eOKN1QXr9+LaVLl5Zy5cqxywklERwcLJUrVxaVSiUVK1aUAgUKSIkSJaRp06Zy8OBBCQ4OFm9vb7G2tpZ+/fole1U1PTffp9SbN28kZ86colKpREdHRxo3bizu7u5y5coVCQgIkKioKJk0aZIUKFBApk+fru1wKZ3w8/MTHR0dUalU0qZNG/nll1/k8uXLyhDaFy5cEGtra+nUqZPSzZYyp2HDholKpZIiRYpItmzZpGzZslKwYEGZNGmSnDt3Try8vOTnn3+WqlWryvLly7UdrtZ5e3tLSEiIiCQ9GV+3bp0YGhrKmDFjkqz38e/OsmXLxNLSUjZu3Jj2AdO/yuz7lMlVFnDjxg1p3ry5GBoaSqdOnaR///6SO3dusbGxkTZt2sjSpUvl2rVrkj17dmnWrJlcv35d2yFTOpKYmCiXL1+WmjVrirOzszx9+lSWLVsmLVq0EEdHR8mRI4cMHTpUatWqJSYmJtKxY0fl6ntmlJCQIH379pVixYpJx44dZciQIdK4cWNxdHQUW1tbGT58uAwfPlwGDx4sOXPm1OjeQ1nXtWvXpHz58qJSqWT06NFSsWJFcXJyEnt7exk2bJicPHlSdu7cKdmyZZPBgwdr3FOTmS5O0Afdu3eXsmXLysKFC+XUqVMyadIkcXNzEyMjI6lbt64UK1ZMihUrJqamprJp0yZth6s1t2/fltKlS0uPHj0kNDRURJKejP/xxx9ibGwsa9asUco/PQm3sLCQ7du3f7/A6bOywj5lcpVFeHt7S4cOHcTBwUH8/PwkNDRU9uzZI23bthVnZ2cpUaKE5M+fX1QqlXTo0CFJtxXK2hITE8Xb21sKFSokP/74o0RGRorIh4Pk4cOHpX379tKgQQNRqVRStWrVTDl/lcg/3RMSEhKkd+/eUrt2beX+KnVXwWbNmknhwoXF0tJSVCqVWFtbJ5nDhrKexMRE8fHxkbJly0qNGjUkMjJSnjx5ItOmTZPmzZuLiYmJ/O9//xNdXV1RqVQyZMgQHoczoY+7R7ds2VJKliwp69atU8pu3bolf/75p7Ro0UIcHBzE0tIyS7dkxsbGysSJE6VKlSrSv3//ZE/Gw8LCpG3bttK7d29JTExM9iR827Zt3z12Sl5W2KdMrjK5jz+Qvr6+0rhxY3FwcFCuiqrvm9m+fbvMnTtXKlasmOVvlqXP8/HxkcKFC0v58uUlOjpaKVcfFI8dO6b8P7MnWPHx8crN57/99pvSxSEyMlIiIyNl+/btMm3aNLl586Y2w6V0xsfHRwoWLCiVK1dWjr9xcXHy4sUL2bRpk/Tu3VtKlizJ0SQzsY9PItu0aSPOzs6yZs0a5Rii9vTpU3nz5s33Di9dCAoKkhcvXojIh2Pub7/9JhUqVNA4Gf84Ue3Ro4e0a9dO4zk2bdokRkZG6bZ1I6vJSvuUyVUWc/36dWnatKnY2dmJj49PkuW8Ukr/xsfHR4oUKSIVKlRQWrA+/dxk5lEBRf7Zvvj4eOnXr5+UL19epk+frvxAEH2J+jtUrly5ZEfhDAsL00JU9D19fIxs27at/PDDD+Lp6akMv5+Vbdy4UWrWrCm2trbSqFEjOXLkiIiIzJw5U1xcXKR///4SHBys1A8LC5N69erJ5MmTNZ7n5s2b8vfff3/P0Okzsto+VYl8ZqYuynDUu/LfZmq/ceMGJkyYgEuXLuHw4cMoUaIE4uPjoaenBxHJsjO909fz9fVF+/btYWVlhWPHjsHY2FjbIaWZz30n1DPBJyQkYNCgQbh8+TJat26NPn36wNzcXAuRUkbi6+uLdu3awcLCAqdOnYKRkRHi4uKgr6/P43AWoT6GAEC7du1w+/Zt9O/fH506dcrUx9QvWb58OYYMGYJBgwbBxMQEy5Ytg7W1NbZv344CBQpg5syZOHDgAHLkyIGpU6fi3bt3WLhwIZ4+fYrLly8r5zEiokzaTtqVJfeptrI6Sj3qbkofN6feunVLAgICPrvO9evXpWXLlmJoaMhuSyQiImfPnlX+P2XKFFm8ePEX6/v6+kq2bNmkR48eaR2aVn069PHHXW0/bsEaNGiQFCpUSObPn88BCEjjM/C5z4OPj48UL15cnJyckm3Boszp0znP1Bo1aiQuLi5Z9h5NT09P0dHRkX379illt2/fFpVKJbNnzxaRD+c5q1evFldXV9HR0ZGKFStK06ZNs8RcihlRVt2nbLnKJPz8/NCuXTucO3cO+/fvR58+fbB//36ULFnys+v4+Phg7ty5mDBhApycnL5jtJTeBAQEwMnJCXXr1kXevHnx559/4tKlSyhatOgX13v48CHy58+vXH3NLF68eIEjR45g//79iIuLQ/Xq1dG4cWMUKVIEgGZr1sctWKNGjUK/fv2QP39+bYZPWiQpbHXy8vJC//79sXHjRn5uMhH158DHxwfPnz9HUFAQatasiXz58iWp+3EL1suXL+Hg4PCdo9W+qKgoFCpUCLa2tti4cSMKFy6MxMRE6OjooGzZsmjevDlGjx6t8Vvj4+MDW1tb5MqVCzo6OkoPHEofsvI+ZXKVSVy6dAkDBw7Eu3fv4Ofnhw0bNqBVq1b/ul5sbCwMDAy+Q4SU3vn4+KBy5crQ19fHxYsXUaxYMY0f/S/52noZwc2bN9GhQwfkzp0bERERsLKywp49e1CxYkUMHz4cLVq0SLJOZtp++u/UJ9QnTpzAzp078e7dO5QsWRKDBg36YjevmJgYGBoafsdI6XvYsWMHevfujXLlyuHevXvIkycPfvrpJwwbNixJ3ax8DNm+fTucnZ0REREBd3d3lC5dGsOGDUOFChWwe/duNGvWDJcuXUKFChU+e/FCfdJO6UOW36faajKj1Ddv3jxRqVTi6OiojOT2cVdBoi+5ePGimJiYiKWlpbRq1Uop/9Ls6JmNr6+vWFhYyIgRIzRmfD9//rzkzZtXypQpI4cOHdJihJTe7dixQywsLKRr164ybdo00dfXlxYtWkhgYKC2Q6M09vGx0cvLS3LlyiUrVqwQEZGrV6+KSqWSmTNnaiu8dOn3338XPT09OXbsmIiInDlzRgoUKCBdu3aVWbNmiZmZmXh6eopI5h2BNrPhPuVogZmC+sO5f/9+mT59uri6ukrRokXl9evXIsIRACl5yR3UXr9+LRcvXpQcOXJIs2bNkizPzInVrVu3xNzcXJkVXt3PW/39uXLlilhbW0vLli21FiOlb8+fP5eiRYvKggULREQkNDRUcuTIIT///LNGvcz8PcqKzp49q4x0pj6urlu3TlxdXUVE5MGDB5I/f37p2bOnss6jR4++d5jpzrJly0RPT0927NihUX7p0iUpUKCAqFQq5XgsknlPxDMT7tMPmFxlYOof6JiYGI2boa9cuSKVKlWSokWLytu3b5XyU6dO8eopiYjmAe348eOyfft2efLkiVJ27NgxyZEjh0Yi0adPH1m1atX3DPO7SExMlLi4OPnxxx/F0tJSLl++rCz7eNJgEZFVq1aJSqVKdhoDoocPH0rZsmUlMTFRnj59Kvb29tKrVy9l+YULF7QYHaWFI0eOSIECBWTSpEkaA1EsX75cOnbsKBEREZI7d27p1auXchw5ePCgzJgxI8sOXCEismLFCjEwMJCdO3dqlC9btkyioqLk6tWrUqBAAWnfvr1cvXpVO0FSinCf/oPJVQalTqz27dsnzZo1EycnJ+nbt69s3bpVRD5cJahWrZoUKVJEfHx8ZOzYsZI/f37x9/fXZtiUzgwfPlwsLCzE0dFRDAwMZOnSpcoP/rFjx8TGxkaKFy8ulStXloIFC2bKbqbq0QDv378vZcuWlXr16snx48eV5R/PDu/j4yPGxsZKdwcikX+S7ydPnkiBAgVk06ZNUqBAAenVq5fynbl586bUqlUr059UZEUDBw6UChUqyJQpU5QWrHPnzolKpRJDQ0MZPny4Rv1+/fpJixYtsuy8eCdOnBCVSiWTJk3SKG/cuLGUL19emTj5zJkzUrBgQWnfvr2cP39eG6HSV+I+1cTkKgPbs2ePmJiYyMSJE2Xnzp1St25dyZs3r3h5eUliYqJcvnxZatWqJTY2NlKoUCGNK/KUNX3cHen8+fNSvnx5OXPmjAQFBcmECRPEwsJCZs2apZwgPHr0SPr37y9jx45VThIz4rCon3Pt2jVp06aNcuB/9OiRlCpVSurVqycnTpxQ6qlPnnfs2CHFixfXuB+Lsib1d+nChQuybt06ef36tSQkJEiXLl3E3Nxc/ve//2nUHz16tFSqVElevXqlhWgpLXzc5f7nn3+W6tWrayRYM2fOFCMjI1m5cqVERkaKn5+fjBo1SrJnzy63bt3SUtTad//+falevbo0adJErly5IiIiLVq0kJIlSyo9KNS/N2fPnhVTU1MZN26ctsKlr8B9qomjBWZAIoL379+jdevWqF+/PoYOHYrIyEgUKFAA7dq1w7x585S6CQkJ8PLygqOjI+zs7LQYNaUn8+fPx6tXrxAfH485c+Yo5VOmTMGcOXMwbtw4dOnSBTly5NBYL6MOi5qca9euoUKFChgzZgwmTpyojNb16NEjNG/eHLa2thg1ahRq1aoF4MP3buDAgXj9+jVWr14NU1NTLW8BaYv8/+hW27dvR/fu3TFkyBA0b94cxYsXx99//41x48bB2toa3bt3h7m5OQ4dOgRPT0+cPn36i9NjUMai/hx4e3vj+PHjWLBgAeLi4jB48GAMGDAAcXFxmDFjBmbPno18+fLB3NwckZGR2Lx5M8qUKaPt8LXqwYMHGDRoEHR1dRESEoKIiAjs2LED+fLlU97XxMREPH/+HLq6urCzs8uyoylmFNynH9FeXkffIiYmRlxcXOT69evy5MkTsbe317hZ9uDBg3Lnzh0tRkjpWceOHUWlUknt2rUlLCxMY9mUKVPE2tpaPDw8lCuwmY2Pj4+YmJho3FgrIsp78fDhQylZsqRGF8FJkyaJjY2N3L59+7vHS+nPpUuXxNraWlasWJGku+zOnTulffv2YmZmJqVKlRJXV1e5du2aliKltLR3717R0dGRqVOnym+//SZ169aVQoUKya+//irh4eEi8uE+6A0bNsixY8fk5cuXWo44/bh//77UqVNHLC0tZcuWLSKieT9wvXr1pEKFCsrfmanXRGbFffoBk6t0TN3t5NOuXFevXpXg4GApWbKkzJ49WwoVKiQ9evRQPqR+fn7Spk0b2bVrF0elos+OxjNkyBDR0dERT09PjQFRRD7ci1WvXr1M+fm5deuWGBkZydSpUzXKly5dKgsWLFDei0ePHknJkiWlSZMm0q5dOzE0NBQvLy9thEzpiPo7MXfuXKldu7ZG17BPkyw/Pz95//59lr23JjNLTEyUiIgIqVu3rvzyyy8ay/r27Sv58uWT6dOnZ+lBK77Gw4cPxc3NTRo0aCCnTp1Syhs0aCCFCxfmaMcZEPcpk6t07cGDB8r/4+Li5OXLl2Jraytnz54VEZHFixeLSqWSunXraqw3ZswYKVq0qDx79uy7xkvpz8eJlY+Pj1y6dEljxLIePXqIsbGxrFu3LkmClVxyn9G9f/9eqlSpInnz5tVolZs+fboYGRnJmTNnROSfq2mPHj2S/Pnzi7GxMUcIJBH55/swfPhwqVq1qkaZ2qVLl5T7+Chzc3Nzk4EDB4qIZnJds2ZNyZMnj4wePZoJ1r+4f/++1K9fXxo2bChnz56V5s2ba5yEZ8aBlDK7rL5PM+jUx5nf3r17UaNGDezduxcAoKenBwsLC1hYWMDGxgYA0KZNGwwaNAhHjx7FxIkTMWXKFPTp0weLFy/G+vXrkSdPHm1uAmmZiCizm48ZMwbu7u5o1aoVBg4ciBYtWgAA/vjjD3Tu3Bm9e/fGzp07ERUVpayvUqk+O3N6RmVpaYlmzZohX758+OWXXwAACxcuxOzZs7Fnzx5Uq1YNAKCrqwsRQYECBXDixAncunULpUuX1mLklF6ovw9FihTBxYsX4ePjo/EdiY6OxpYtW3DmzBlthUjfQWJiIkQEOXLkwNWrVxEXFwc9PT0kJCQAACpXrozY2Fhcv34dcXFxWo42fXNycsLChQuhUqlQq1Yt3Lp1Czdv3oS+vn6mus83K8ny+1S7uR19zoULF6R9+/ZSsmRJ2bt3r4iIvH37VpycnDRGKnv16pUsXLhQypYtK9WqVZNOnTrJzZs3tRU2pUOzZs2S7Nmzy/nz5yUsLEzGjh0rKpVKo7m+d+/eolKp5O+//9ZipGnHy8tLPDw8lL8XLlwo1apVkzJlyoiFhYUyJOzHLRC///47v0ukfCbu3Lkj58+fl4cPHyrLGjVqJDlz5pTLly9LbGyshIeHy5gxY8TW1lYeP36srZApDag/B3FxcRo9Al68eCE5cuSQtm3barT+DxkyRJYtWyYBAQHfPdaM6s6dOzJw4EClVSOzt25kBVl1nzK5SseuXr0qnTt3lmLFismePXvk7du3YmNjk2x3v4iICBERiY6O/t5hUjoWExMjbdu2lTVr1oiIyO7du8XS0lJWrFghIqJxL8jMmTMz5YHv2rVroqOjI0OHDtUoX7RokRQvXlzq1q0r7969E5F/ulFOmDBB9PT0OHgFiYjI1q1bxd7eXqytraVChQrKXC5+fn7SsmVL0dPTk5IlS0qFChXE1tZWvL29tRwxpSZ1YnX48GHp2bOnVK9eXX7//Xfx9fUVkQ9z/OTIkUPKly8vHTt2lLZt24qhoaFGIk4pkxl/i7K6rLRPORR7OqQeEtrf3x/379+Hp6cnvL290a1bN2zZsgWurq7Ily8f4uLiICKIiYlB/vz50aJFi0zXjYu+TWxsLMqXL4+xY8ciW7ZsaNGiBX777Tf06dMH8fHxmDdvHgoWLIjmzZsr62SmJvtr166hcuXK+OWXX/Drr78mWb5o0SJs3rwZhQoVwvTp02FnZ4fx48dj9uzZOHv2LMqVK6eFqCk9UB9LX7x4gZYtW6Jnz54oVaoUNm3ahJMnT6JmzZqYPXs2AGDr1q3w9/eHqakp6tSpg3z58mk3eEp1u3btQocOHdCrVy8kJCTA29sb2bNnx/jx41G+fHkEBgbCw8MDwcHBEBGMHz8eJUqU0HbYRKQFTK7SqS1btqBfv344f/48goKCsGzZMvz9998ICAhA27Zt8fDhQ4gIDAwMEB8fD09PT/zwww/aDpu0KDExUbnH6uOyX375BY8ePcKZM2cwa9Ys9O7dGwDg7++PXr16oWnTpujRo4c2Qk5TDx8+RIkSJTBs2DBMmTJFOVn+66+/kDt3bmX+KnWCVaJECRgYGGDFihVMrAgA4O3tjZUrVyIqKgpLliyBsbExwsLCsHDhQuzYsQPVq1fH/PnztR0mpbFr166hRYsWGDVqFHr06IHw8HA4Ojoie/bsKFSoECZNmgQXFxflGBMXFwd9fX1th01EWpI5Lk9nEuoDc1RUFC5cuIAxY8agcOHCAD7cYG9gYIDz58+jZ8+eyokhAMTExMDQ0FBbYVM68HFidefOHRgbG8POzg6GhoaoX78+2rZti7Jly6JBgwYAgMDAQPTs2RMhISHo2rWrNkNPE4mJiVi1ahXMzc2RPXt2AB8GIpg6dSoWLlyI/fv3K3UHDhwIHR0dzJs3DwEBATh37hzKli2rrdApnYiNjcWff/6JnTt3wt7eHsbGxgAAc3NzDBgwAMCHgYe6d++OlStXajNUSmXqa84qlQoxMTEAADc3N3Tu3BnPnj1D7dq10a5dO9SqVQv9+vXDpEmTMHr0aFSvXh0AMk3LPxH9N2y5SmcuXLiAtm3bIm/evJg+fTqqVq2qLLt8+TIWLVqE69evY/To0Wjbti0AsCsgKUaPHo1Vq1bB0tISNjY22L17N3LkyIGtW7eid+/ecHJyQmxsLExMTBAdHY2LFy9CX19f6Yqamfj7+2PWrFm4ePEiunTpgtDQUMyePRtr1qxRksyPk9JVq1bB1dUVBQsW1GbYlI74+/tj9uzZ2LBhAwYMGIBx48Ypy0JDQzFz5kxcuHABGzduRK5cubQYKX2r5Fr+d+/ejbt376Jnz56IiIhA7ty50a5dOxgaGuKPP/6AgYEBatasiXv37qFGjRrw9PRUknAiyrqYXKUzd+/eRa9evXD27FkcOXIEP/74o0YXA29vb0ydOhWvXr3C0aNHYWpqquWIKb04evQo+vbti0WLFuHNmzf4448/8PjxY5w5cwb58+fHhQsXcPfuXTx58gRFixZFq1atoKurm6nusfpUQEAAfv31Vxw5cgSPHj3C4cOHUbt2bY1kMrmTKsp6Pu3S9fG9r9OnT8fVq1fRtGlTjBw5UlknLCwMsbGxSusoZUzqY8Ddu3dx9uxZ9OjRA7dv34aLiwsWL16MTp06QUdHB+Hh4ahWrRp69eqFfv36ISoqCn369EHx4sXRsWNH2NnZaXtTiCgdYHKVziQmJuLevXvo2bMnAgICcP78eeTMmVPjBNjX1xc5c+aEvb29lqMlbfo0KTh16hQuX76M4cOHAwAeP36MHj164P79+zh79myyN9lnxharTwUGBmLatGk4efIk3N3dMXToUABZY9vp66gTq6NHj2Lr1q149eoVatWqhebNmyNv3rx4/vw5Zs6ciatXr6Jly5YYNmyYtkOmVKI+jvr6+sLFxQWenp5wcnLC48ePceXKFfz222/K5+Pdu3do27YtHB0d0bFjR5w4cQLbtm3DqVOnkDNnTm1vChGlE0yutEh9wA4ICICenh5iY2Nhb28PEcH9+/fRqVMnhIaG4uzZs8iRI0embmGglPm4K+jcuXPx8OFDXLhwAS4uLli6dKmSdD158gQ9evTAo0ePcPz4cRQoUECbYWuNugXrypUraNasmdL6wFYrUlOPBqfuAvbo0SMYGhpi8eLFKFiwIJ4/f47Zs2fj8OHD6Nu3L37++Wdth0zfSP39v3HjBipVqoQBAwZg5syZqFy5Mi5duoS6deti3759GoNTrFixAn/++SdevnwJQ0NDbNu2jfdoEpEGJldaoj453rt3LyZOnIiYmBiEhYVh9uzZaNWqFQDg/v376NixIyIjI3H8+HFeGSMAmgnB9OnTMXPmTNSrVw+PHz/Gs2fPsHfvXlSqVEmp//TpUzRp0gSFChXCjh07tBW21qkTLB8fH/z444+YNGmStkOidMLHxwdt27bF8OHD0aNHDwQEBKB48eIwNTVF3rx5sXr1ahQsWBDPnj3DkiVL0K9fPw63nsGpj6O3b9+Gq6srateujc2bNwMA3rx5g549e+LcuXPYv38/KlasqLHugwcPEBsbC2tra3YFJKKkvsNcWvQZe/fuFTMzM5k7d654eXnJkCFDRFdXV5YsWaLUuX//vhQqVEgqVKigMSs80ePHj6Vnz55y9uxZEREJDg6WBg0aiIODg1y5ckWjrr+/v8THx2sjzHTl1atX0qVLF6lTp468fftW2+FQOnHy5Enp0KGDiIg8ffpUChYsKD169JANGzaIvb291K1bV+7duyciWWsizMxK/Vvq6+srJiYmUqBAATE3N5fjx48rdYKCgqRq1ari5OQkd+7cEZF/JhMmIvoStlx9B8l1PfL390e3bt1Qt25dDB06FM+fP0fNmjWRLVs2+Pj4YO7cuRg0aBBUKhUePXoEHR0d5M+fX0tbQOnNli1b0LZtW+TPnx/r169XWqqioqLQsmVLXLt2Dbt3704yVxPvM/pwDxYAju5GGh49eoSCBQuiZcuWMDExwdq1awEAVapUwY0bN1C1alXs3bsXenp6HJ01E/Dx8UG1atUwePBgTJgwAf369cOGDRuwf/9+1K5dGwAQHByMxo0b4+3bt9izZw+cnZ21HDURZQS82SCNqROrZ8+eYdeuXUhMTATwYf6MH3/8Ee7u7ggICICbmxtq1aqFK1euoGvXrhg9ejTmzJkDAChYsCATK9LQunVrtG/fHk+ePMHNmzeVuViMjY2VewAqVqyIu3fvaqyX1RMr4ENSxcQq61JfTwwNDUVQUJBSXrBgQbx+/Rp3795FvXr1AADh4eEoVKgQZsyYgdWrV0NfX5+JVQYnIkhISMCIESMwYMAA/PrrrzA0NMTMmTPRvn17NGrUCCdOnAAAZMuWDfv27YOtrS2qVauGBw8eaDl6IsoIODpCGtPR0cHLly9RpkwZ5MuXD5GRkWjTpg3s7Ozg7u4OGxsbTJo0CXny5MHs2bOhUqlgZ2eH7NmzY/r06ejWrRusra21vRmkRZ8bdGHdunWIiIjAyJEjYW9vj7p160JfXx/GxsbYtGkTPDw84OTkpIWIidIvlUqFXbt24bfffkNQUBAaNmyIQYMGIW/evDA1NYW1tTX27duH4sWLY8uWLfD19cWsWbNga2ur7dApFahUKujq6mL37t0wMTFRynPkyIHffvsNKpUKDRo0wIEDB1C7dm1ky5YNO3fuRIcOHTj4DRF9FXYL/A4uXbqEypUrw87ODuXKlVNaHXR0dJCYmIgOHTpApVJhw4YNAIAhQ4agatWqqFu3LiwsLLQcPWmTfDQq4IYNG3Dv3j04ODigcOHCqFmzJgDgp59+wsWLF7FmzRolwfoYuwJSVvfx9+jixYto3LgxunfvDisrK8ycOROurq6YPHkySpUqhVWrVmHJkiV48eIFTExMsH37do4Gl0moPwcffx4+FRQUhBEjRmDdunU4ePAgatWqBYAjixLR12Ny9Z14eHjAy8sLERERiIuLw4ABA9C6dWvo6Ohg7ty5GDVqFIYOHQo/Pz8cOHAAFy9eZP/uLO7jE4AxY8Zg0aJFqFChAvz8/KCvr4+2bdvCw8MDAPC///0Ply5dwtKlS9GkSRMO2U8EJJm+4uHDhzh16hQCAwMxZswYAMC9e/dQt25dlChRAvPmzUPhwoXx4sULvHz5Eo6OjpxPMBO4f/8+8ufPD319/S8mVmpBQUEYM2YMVqxYgZMnT6JGjRrfKVIiygx4GSaVqe+pUouLiwMAFCtWDCYmJvj999+RK1cuLFiwAJs2bUJCQgKGDBmCUaNG4ciRI3j37h1OnjzJxIqUE4CrV6/iwoULOHToEI4fP46jR4+iU6dO+PPPPzFz5kwAwO7du1G4cGGsXLmSiRURgJkzZypTDyQkJCA4OBjOzs7o2bMngoODlXrOzs44fPgwrl+/jmHDhsHHxwe5c+eGi4sLE6tMYOPGjWjYsCH27NmD+Ph4peXqS6ytrTF16lQMHDiQU6AQUYqx5SoVqbsNPH/+HN7e3qhXrx6MjY2V5WXKlEGTJk0wevRotGzZEsHBwRg4cKDSgvX+/XsYGhpqrENZ25IlS3D8+HGEh4dj586dyj0CAQEBmDdvHi5evIh169bB0dERALuuEKm7wbq7u2P48OEoUaKEUnbu3Dk0atQIpUuXhqenJ/Lly6e0ZNy9exflypVDkyZN4OnpCUNDQ21vCqWCyMhINGrUCFFRURg5ciR++ukn6OnpfVULFo+nRPRf8KiRinR0dODn54dixYqhbdu2qFatGnbu3AkfHx8AwLRp0+Dj44P4+Hh4enrCwsICy5Ytw5o1a5CYmAgrKysmVqRBR0cHhw8fxpUrV3Dr1i2l3NbWFm5ubrh48aIytLi6/qetp0RZRWJiInR1dfH27Vt4enqiRIkSOHPmDDZs2ICwsDBUrVoV+/fvx4ULF+Dh4YEXL14oLRlFihSBj48PJk+ezMQqk4iPj4eJiQkOHDgAc3NzTJs2DXv37v3qFiwmVkT0X/DIkcrevXsHBwcHlC9fHqampli7di169eqFqVOnAvjQ53/fvn3IkSMH1q1bh7i4OGzZsgXh4eFajpy0LbmkqG/fvvjzzz+hp6eHP/74A3fu3FGW5cuXD3nz5lWGYVfjCQFlRepWhmvXriFnzpxYuXIlAGDu3LmYMGEC9u7di/DwcFStWhVHjhzBpk2bMHbsWLx8+VI50S5cuDBH2MxE9PT0kJCQAGNjY+zZswdWVlYpTrCIiFKK3QLTwPnz5zF69Gg4OTmhcuXKKFiwIEaMGIFixYphzZo1KFeuHI4cOQIrKysEBQUhIiJC6dZFWdPH3U98fX0RGRkJS0tLFCtWDADw559/wsPDA5UrV0bLli1hZ2eHWbNm4cWLF/D29uZogJSlqb8/vr6+ysSw6gtaANC+fXv4+vpizJgxaNq0KczMzHD69Gk0bNgQ9erVw5IlS2BnZ6fFLaDvITIyEk2aNEFISAjGjBmToi6CRERfi8lVGjl58iTGjx+P7NmzY/LkyXB2doaXlxeWL1+OOnXqoFOnThwimwBojgo4atQo7NixA4GBgXB0dISjoyMOHDgAlUqFVatWYdiwYXj//j1atWqFbNmyYfHixcrVWX6WKCtSf39u3bqFChUqYPTo0Rg/fryy3M/PD3ny5EGrVq1w9+5djBw5Ukmwjh07hvbt28PHx4eDV2Qi6s/E8+fPERISAhsbG1hZWcHQ0JAJFhGlOSZXaejMmTMYPXo0smXLhqFDhyrzEhElZ8GCBZg8eTJ27NgBKysr3Lt3DxMnToSJiQmuXr0KANi8eTMGDRqEXr16oXPnzihUqBATK8rygoODUbVqVejr6+PUqVOwsrIC8OE+1wMHDuDvv/+GiYkJWrRogfv372P06NFo3LgxLCwsEBUVxXtdMxF1krRr1y4MHz4ciYmJiImJQZ8+fdCqVSs4OzsrCVZERAR+/vlntGzZkqOsElGq4c0Zaah69eqYPn06goODMX/+fBw9elTbIVE6cffuXQBQ+vvHx8fD29sbffr0gaurK0qVKoWWLVti7dq1CA8PR//+/QEAbdq0wbRp07B69WosW7YMd+/eZWJFWV62bNnQrFkzGBkZKdMTLFiwALNmzcL48eOVUTa3b9+OokWLYsSIETh06BBEBEZGRtoMnVKZSqXCoUOH0KVLF/Tv3x+3bt1Ct27dsHDhQixYsAC3b9+GiYkJ9uzZg7i4OKxYsQLR0dHaDpuIMhG2XH0D9RWy4OBgZMuW7bP1zpw5g/Hjx0NHRwceHh5wdXX9jlFSerNw4UIMHjwYZ86cQdWqVZXyOnXqwNjYGHv37tWoP3LkSFy5cgUHDhxQTgTXrl2L3r17Y+jQofDw8IC+vv533Qai9OLj+xUnTZqEffv2wdTUFL6+vti/f7/yHfu4hdfd3R0TJ05EgQIFtBY3pY2goCB069YNZcuWxYQJE/Dq1StUq1YNNjY2eP36NerUqYMhQ4agSJEiiIyMxNu3b5EnTx5th01EmQhbrv4D9ahuKpUKR48eRdeuXREbG/vZIbCrV6+OCRMmwNDQEAULFvyeoVI61LZtW7i7u6NBgwY4d+6cUt6kSRO8efMGhw8f1qhfoEABhIeHIzY2Vmnpcnd3x8qVK9G5c2cmVpSlfTz9gIeHB5o2bYoHDx6gfv36KFq0KIAPF8J0dXURHx8P4MPFCSZWmZOpqSm6dOmC9u3b4+3bt6hTpw5+/PFHXLx4Ea1atcK2bdvw66+/4tatWzAxMWFiRUSpjslVCsybNw8nTpyAjo4OEhISAACnT5+GmZkZDAwMvrhu7dq1sWPHDuTOnft7hErpWM6cOTF79mw0bdoU9erVw9mzZwEADRo0gI6ODpYuXYodO3YgMTER7969w/bt21GwYEGYm5trDB3cvn17DhtNBM0Ea+zYsejTpw8ePHiAWbNmISAgQPne8L6azOfTzjeGhoaoUaMGChUqhPXr18Pe3h4zZswAAOTNmxfZs2fHmzdvkD17dm2ES0RZAJOrrxQUFISTJ0+iWbNmOHfunNK95PXr18qEk/82vxD79pNajhw5MGfOHDRv3hxubm44ffo0nJycsHz5ckRERGDs2LGwt7dH3bp1ERgYiLVr1yoniBzRiiipjxOs8ePHo0mTJjh8+DAWL14Mf39/fm8yIfXx0MvLC/v378e+ffsAANbW1gA+DHQSHh6uzAX47NkzDBs2DBs2bICtra3W4iaizI2X8b6StbU1Zs6cienTp+N///sfdu7cierVq0NHR0e5WTo6OlpJoJI7CeaPe9b18X0hajY2Nli6dCkSEhJQv359HDx4EK6urli7di1evHiBc+fOwcHBAS1atFC6NPHKO5Gmj4+16gRLfX+rjo4OVq1aBQMDA4wdO5aDv2QyKpUK27dvR5cuXWBnZ4eXL1+iWbNmWLNmDXR1dZE7d24EBQVhwIABEBEcPnwYXl5eSvJFRJQWeKb2FdQ/1kWKFMHYsWMRHx+PZs2a4ejRo8iTJw/Cw8MBQOnPDwAvXrzgxMAEQDOx8vT0xN27dxEeHo4ff/wRzZo1w6pVq6Crq4sGDRrg8OHDqF69Ouzt7VGxYkXlORISEphYUZanTqRiY2MRHx8PExMTJbFSIgfBwgAALfhJREFUf88+TrDGjx8PAwMDtG7dmolVJqL+HERGRmLJkiVYunQpatSogbt378Ld3R3NmjXDnj170KNHDwQFBeHGjRuIjIzEpUuXUKRIEW2HT0SZHEcL/ArqA7l6PpQnT55g7NixOHz4MAwMDGBmZgbgQ8uVmZkZ4uLiYGpqitOnT8PS0lLL0VN6MWLECKxduxYdOnTA8+fP4eXlhf/973+YO3cu3rx5g2HDhmHnzp3YtWsXateure1widIV9XF4//79WL58OZ4+fYqyZcuiSZMmaN68OQDNCxmc/y1zO3LkCFavXg0DAwPMmDFD6eZ38eJFNG3aFOXLl1e6CQJAXFwcB/8hou+CydW/UP+gHzx4EHv37kWbNm3g6uqKO3fuYOHChfD09MTAgQPRuXNnPH36VBnoonjx4hyNihSHDx9G3759sWnTJlSsWBHbtm1Dp06dsGLFCnTq1AkAEBoainbt2iE6OhrHjh3TcsRE6c/+/fvRvHlz9O/fH7ly5cKuXbugo6OD//3vfxgxYoS2w6PvaNu2bejSpQtMTExw584dZM+eXfm9vnTpElq2bIkCBQrgyJEj/zrgFBFRauKAFv9CpVJh586daNGiBezs7JAzZ04AwA8//IABAwagffv2WL16NWJjY9GoUSM0adIETZo0YWKVxX16zULdTVSdWHXr1g3z5s1Dp06dEB4ejrNnz8LCwgIbN27EkSNHtBQ1UfokIggPD8f8+fMxevRozJ07FyNHjsTBgwdRoUIF7Nq1CwcOHFDqUubXqFEjrF+/HtHR0Rg1ahSAf+5rdnFxwcaNGxEQEIDXr19rM0wiyoKYXP2Lx48fKz/m48ePxw8//KAsK1asGIYPH4769eujXLlyuHjxohYjpfRE/SP/7t07AIC+vj5y586NgwcPomvXrpg1axb69OkDADh69Ch2796Nt2/fwsLCQmPUM6KsTJ0oRUZGwtTUFOHh4UpZQkICrKysMHnyZERHR2P//v0AOHBQZqTe569fv8bz588RExMDY2Nj/O9//8OqVauwfv169O3bV2OdatWq4dq1a5z+hIi+O94h/y8iIiIQExMDFxcXpezj0amKFCmCCRMmwNjYmCMQkYb58+cjICAAM2bMQLly5dCzZ09s3LgRq1atQpcuXQAAUVFRWLZsGRwcHDTmXfm3Yf2JsgKVSoVNmzZh9+7dmD59OkxMTPDw4UNlWUJCAiwsLFC3bl1cuXKFI2pmQurf2127dmHcuHGIjIxEbGwsunbtCnd3d7Rs2RLAh4nVdXV1sXjxYmVdTn9CRNrAX6F/4e/vj4CAANjY2AAAYmNjlf7b3t7eCA8PR40aNbBkyRLeLEsaEhMTsXDhQvTo0QPFihXD+vXr4e7ujtu3b+PEiRMQEcyYMQOBgYHYt28f57Ei+n/q70FQUBA8PDzQr18/5MuXDx4eHqhduzaKFi2KsWPHKvX9/PyQO3dufncyEfVnQKVS4fjx42jXrh0mT56MSpUq4dixYzh48CCePHmCiRMnomXLltDR0UHLli1haGiIOXPmaDt8IsrCOKDFvxARlClTBjlz5sSBAwc0rooOHDgQhoaGmDZtGm+YzeI+TorU/3/z5g06deqEqlWrYuzYsYiNjcWuXbswfPhwiAhsbW1hb2+P7du3Q19fn6ObEX3k8OHDOHfuHAIDAzF79myYm5sDAFauXIlevXqhWbNmsLe3R3R0NDZu3IgLFy6gePHiWo6avpWPjw/KlCkD4J/ugP369UNYWBjWrVun1Fu3bh3mzZuHFi1aYMyYMYiNjcXhw4fh5OTE4daJSKuYXP0/9Qmxt7c3bt68CR0dHTg7O6NChQrYs2cPxo0bB2tra/z+++8IDAzEoUOHsHz5cpw9exbFihXTdviUTsTExMDQ0FD5PA0fPhz79u3DjRs3lMT87du3eP/+PQwNDZWr7ezORKRpypQp8PDwQP78+eHj4wMLCwtl2fnz5zFnzhyEh4fD0tIS48ePR4kSJbQYLaWGNWvWYMOGDdi8eTMsLS2VC1a9evVCYGAgdu/erTHc/siRI7F161Y8ePCAF6aIKN1gcvWR7du3o1u3bihRogTu3r0LW1tbtGjRApMmTcLJkycxZswY3Lp1CzY2NjAzM4OnpydKly6t7bApnfD09MTevXsxdepUODk5QU9PDwkJCXByckLr1q0xY8aMZNf7+GSBKKtSX5D4eGCXxYsXY+jQoZgzZw4GDx4M4J/5q9RdtNUXNCjju3btGiwtLZEvXz4EBAQoc1d5eHhgxYoVuHLlCnLnzq0cM3ft2oXx48fj1KlTvOeZiNINntH9vzt37qBfv36YMWMGTp48CS8vL3Tt2hVr1qzBxIkTUbNmTZw/fx7Hjh3DkSNHcPToUSZWWZz6uoR6ZL/AwEBERkaifPnyGDx4MLZs2QJdXV10794dDx48gL+/f7LPw8SKsjp1YrV371706NED27Ztg0qlQs+ePTFlyhQMGTIEK1asAADo6uoiMTFRuceVXbIzBxFBqVKlkC9fPvj6+qJZs2ZYs2YNAGDSpEnInTs3fvrpJzx9+hTx8fEAgOPHj8Pc3JyfASJKV7J8PyT1FbAHDx7AysoKrVu3hp6eHvLmzYsuXbogPj4emzZtQuvWrVG0aFGUK1eON02TRmtTbGwsjIyMMHLkSIwcORKenp44evQoevfujWPHjsHBwQGHDh3C8ePH0bFjRy1HTpT+qFQq7NmzB61bt8bUqVNRoUIF6OrqwtTUFL/88gsAoE+fPkrC9fEFCR6PM67kWu3DwsKQLVs22NjYwNPTE3p6eujQoQO2bduGVq1awcXFBc7OzjAzM8O5c+dw6tQpmJmZaWkLiIiSylKXzNUtDAkJCUpZYGAgAMDa2hphYWG4d++esix79uxo2rQp7t+/j0ePHgHgDzlpnhAsXrwY7u7uaNy4sdJtqUuXLli8eDEOHTqEBw8e4OrVq4iKisLOnTsBcJJTok8FBATg119/xbRp0zBs2DA4OTkB+PBdMTY2xi+//IJff/0VvXv3hqenp3aDpVSjo6OD+/fvY+fOnVCpVNi6dSvq1auH3LlzY9KkSciVKxd+//13bN68GXnz5sXly5cxYsQIVK1aFWXLlsXly5fZg4SI0p0s1XKlo6ODe/fu4cCBA/jll1+wdetWDBw4ED4+PrC3t4e1tTU2bdqEvHnzwsHBAQBgZ2eHokWLMqkihTqxGjVqFFavXo1hw4bBxMREuSfv8OHDsLS0hIuLC/bu3Ys7d+6gUqVKGDFiBAAm6JS1LV26FLa2tmjevLlGeWBgIAoVKqRRpv6u6OnpYdSoUdDX10fFihW/W6yU9v766y/8+uuv+OWXXzBv3jysXr0aurq6KFOmDEaOHImZM2di0aJFiIuLQ8eOHTF06FBth0xE9EVZKrkCgBMnTmDo0KHw9vbGxo0b8X/t3XlcVfW+//HXBgRRFExUkBQzOWo5pmmU5YATlqZo4ixax+MVpwjn+RA4W2l4U0NNr4rHmdTUY+aAXQUNJE444ewVxRxCRIHN+v3hj30kzaOFbcD38/Hg4WOv9V2Lz0Ld7Pf6ru/3GxERgbu7OwCTJ0+mf//+mM1mOnbsSPXq1Zk/fz7nz5+nbt26Vq5cCpL4+HiioqJYt24dTZo0ISoqCsMw6NSpU57HXEqWLEnDhg1p2LAhgGYFlGfa1atX2blzJzNnzsyzPTU1lfT0dMs4qvsnqUhISCAhIQF/f3+Cg4P/9Jrl6QoJCSEmJoZ58+YxaNAg+vbtaxmDd3/AioiIICsri379+gFoTUARKbCK/GyBuY9w3b+GUEBAAMuXL6dz58784x//yNN+3bp1TJ06lbNnz/Lcc8+RmZnJ+vXrLetuiADs2LGDwMBATpw4waZNm+jVqxezZs3ib3/7G7du3WLz5s1069bN2mWKFAj3fxDOyMjA0dGRmJgYTp06Zfl/0rVrV6Kjozly5Ihl0XaAjz76iEuXLrFo0SJKlixplfrl6cj9d9GqVSsyMzM5cOAAy5Ytw9/fP8/+uLg4xo0bR05ODmvWrLGseSYiUhAV6VvoucHqzJkz7Nixg9q1a+Pt7Y2NjQ3+/v5ERkYydepUBg0ahLOzM4Zh0LlzZxo3bszVq1f55Zdf+Mtf/mKZDlYkV+XKlfHy8uLTTz9lwoQJzJ49mwEDBgDw448/smnTJurUqcNLL71k5UpFrCv3fTg1NRU7OzvKlClDWloaEyZM4JdffsFkMuHv709oaCgBAQG8/PLLzJs3jzt37hAfH8/ixYvZt2+fglURlBu4//nPfwIQFBREnz59MAyDbt26WfZXr16dRYsWAShYiUiBV2TDVe4v9B9//JEuXbrw8ssvU7FiRQAWL14MQOPGjfnwww8xDIPBgwdbFqm8e/euBskKAImJiSQnJ/Ptt99Svnx5OnbsSK1atXB1deXq1asEBQUREhJiCVYZGRmEhITg5OREzZo1rVy9iPXZ2Nhw8uRJWrduTevWrQkJCaFcuXJ88sknjBo1ioULF2JnZ0fnzp1Zv349o0ePZty4cRQrVgw3Nzf27t1LnTp1rH0Zkk9ye6OOHz/OrVu3uH37Nk2aNAFgzpw5AJZH/zp06MAnn3zCtm3b2Llzp9YzE5HCwSjCkpKSjDJlyhijR482Ll68+NA2s2fPNkwmkxEaGmqcPn3a+Pjjj41KlSoZN2/eNHJycv7kiqUgWblypdGwYUOjQYMGxosvvmiUL1/ecHR0NKZPn24Yxr1/Xy4uLsY777xjzJgxw1i2bJnRokULo3bt2kZWVpZhGIZhNputeQkiVmc2m42JEycaJpPJ8PHxMYYMGWL83//9n2EY9/4PtW3b1mjevLmxZs0ayzFnzpwxbty4Ydy8edNaZctTkPs7dcOGDUaVKlWMmjVrGo6OjsYHH3xgnDp1ytJu5MiRhslkMl5//XWjZMmSxqFDh6xVsojIEyuy4SojI8N47733jMDAwDzbMzMzjfPnzxv/+te/LNvmzJljlChRwqhfv75RtmxZIzY29s8uVwqYBQsWGE5OTkZERISRnJxsGIZh/PTTT8bQoUMNGxsbY8qUKYZhGEZsbKzRuXNno1q1akbz5s2NPn36GJmZmYZhGJaAJfKsi4uLM1xcXIyWLVsabdq0MYYNG2ZcunTJMIx/B6wWLVoYkZGRVq5Unrbt27cbzs7OxoIFC4w7d+4YmzdvNkwmk9GjRw/jxIkTlnbr1683Fi5caJw8edKK1YqIPLkiO6FFdnY2LVq0oGvXrgwePBiA7du3s23bNhYvXkzZsmXx9PRk165dmEwm9u7dy40bN6hTpw5VqlSxbvFiVV9++SWDBw8mMjKSjh07Wh4xNf7/4yzBwcHMnTuXLVu20KpVKzIyMsjKyqJYsWI4OjoCmhVQnl3GfZNXGIZBTk4Otra2TJw4kdu3b1OiRAm2bt3Km2++yahRo3Bzc+Po0aOMGDGClJQUxo4dS6dOnax8FfI03Lx5k+DgYCpXrsyECRM4ffo0rVq1om7duuzcuRMfHx/CwsKoUaMGoBkBRaRwKrKLCN++fZvU1FQSEhI4duwYU6dOZdiwYZw/f56QkBDGjx/PhQsXCAoKAuCtt96iQ4cOClbPuNOnTzNgwAC6du1Kx44dAfIEK4CRI0fy8ssvM3v2bLKysihevDilS5e2BCvDMBSs5JmUk5ODyWTi2rVrXLlyBZPJZFmawNPTk+joaEaOHEnv3r2Jjo5m+vTppKSkUKNGDaZNm0aVKlVo0KCBla9CnpbixYvTqlUrevbsyc8//0znzp1p1qwZ69atY/78+WzcuJGRI0dy6tQpQGsCikjhVGQ/AZYuXZrw8HDatGnDjh07uHbtGjNnzsTHx4dq1aqRlZXF6tWruX79urVLlQLkhRdeYNq0aUycOJFGjRpZej3v/yVfvnx5GjRoQHx8/EPvrOoDgTyrbGxsOHHiBL6+vhQvXpywsDCqV69O9erVef/991mxYgUhISFMnz6dtLQ0Nm/ezKxZswgKCuLll19m5cqVlrWupPD79fujg4MD7dq1w8nJieXLl1O8eHEmT54M3HvffOuttzhy5Ij+DYhIoVZkwxVAixYtOHXqFFeuXMHT0xNXV1fLPltbW5ydnalUqRK5T0bqQ7HAvZ4pk8nEsGHDMJlMBAYGWvblfli4ffs2NWvWxN7e3oqVihQsOTk5LF26lJSUFEqVKsXkyZOpVq0arq6uTJ8+nV69ehEdHU1mZibjx4/HZDKxbNky7O3tCQkJUY9vEZL7XnngwAHi4+MpX748r7/+Om5ubhiGwalTp0hPT8fJyQm4t4SFv78/77//vt5XRaRQK/K/ySpVqkSlSpXybMvMzCQkJIT9+/cTGhqqUCUPGDFiBABDhw4FsAQsk8nEhQsXuHz5Mn5+flarT6QgsrGxYfDgwaSnp1sWYu/evTtjxoyhV69epKens2vXLt5880369evHuHHjcHBwoEuXLpZF3qVoMJlMrF+/nn79+uHu7k52djaVKlVi4cKFeHl50aZNG8LCwujQoQMODg7ExMSwb98+BSsRKfSK7Jir3/I///M/jBgxgkWLFrF582a8vLysXZIUUCNGjGDatGkMHTqUzz//3LJ94MCBZGdnM3DgQCtWJ1Iwubu7M3LkSDw8PDh69CgnT54kNjaWv/3tb5b1A+9fCDY4OFhjXYuga9eusW3bNubNm8eRI0eYM2cOjo6O+Pn5kZSUxGuvvcb27dt5/vnn8fLy4vvvv9d6ZiJSJBTZ2QIf5tixYwwcOJAyZcoQGhqqRV6fcY87E9XMmTMZM2YMc+fO5ZtvvuH48eMkJiZSrFgxzGaz7riLPMSlS5cICwvjf//3f+nVqxfDhw8H4NSpU1StWtW6xclTFR8fz6BBg3B0dGTBggVUq1YNgD179jBt2jQuXLjAmjVrqFGjBpmZmdjZ2VkmPhERKeyeqXAFcOXKFRwcHHB2drZ2KWJFudOrA9y4cQMXF5dHtp89ezYjRoygWrVq/Otf/6JYsWKabl3kP0hJSSE0NJSYmBjeffddxo4dC6CbEkVQ7s2qxMREdu3axZo1a0hMTCQxMREPDw9Luz179jBr1izi4uLYvXu3JXiJiBQVz9ytovLlyytYPePuD1ahoaEMHz6cn376iZycnN885qOPPmLjxo389NNPClYij8nNzY1x48bRqFEjtm7dyqRJkwAUrIogk8lEVFQUXbp0oV69egwfPpznn3+eHj16kJKSYmnXtGlThg0bhre3t3qrRKRIeuZ6rkRyjRkzhiVLljBt2jTatGmDu7v7Yx2nYCXyZFJSUhgzZgwXLlwgMjKSsmXLWrskySe5PVbXr18nMDCQV199lQ8//BCAtWvXMm/ePBwcHFi+fDkVKlSwHJeRkWFZG1BEpChRuJJn0vfff0+PHj1YtmwZb731lrXLESnyLl++DJDnA7YUDXv27GHEiBE4Ojoyc+ZMGjVqBNwLXrkBy8nJiYiIiMe+iSUiUlipT16KvBEjRvDLL7/k2Xb16lUcHR2pV6+eZZ2z3D/NZjNZWVl/ep0iRVmFChUUrIqounXrcvnyZfbt28fJkyct200mE126dGHo0KFcvHiRwYMHYzabrVipiMjTp2ebpEg7efIkhw8fpkSJEnm2m81mzpw5Q2pqKqVLlyYnJweTyYRhGOzatQs3Nzdq165tpapFRAoPFxcXEhISaNiwIdOnT+ell16yTLtvMpno3LkzdnZ21K9fX+PtRKTI02OB8sxYvXo1Pj4+uLq6curUKXr06EHt2rUZPXo0L774IgB3796lbdu2tGrVyjKzmYiI3JM7xio+Pp4ff/wRAC8vL1577TWuXbtGgwYNqFChAgsXLtS6VSLyTFK4kmdCamoqFStWxMfHh8jISFxcXPjss8+IjIzE1dWVDz74gKysLBYtWsSVK1eIjY3VpBUiIg+xbt06hgwZwosvvkiJEiWIjo4mPDycgIAAbty4Qf369fHw8GDevHnUr1/f2uWKiPypNOZKiqRf3zMoV64chw4dIjExke7du5Oens6wYcMIDAzEwcGBrl27Mn36dOzt7YmJicHOzk5jA0REfiU+Pp6BAwcyceJE9u3bx9SpU8nIyCAxMZHs7GxcXFyIi4sjISGBkSNHkpmZae2SRUT+VOq5kiLn1wsEOzk5WXqhEhISaN26NfXq1WPt2rU4OTkBcPbsWUqXLo2Liwsmk0nTrYuI3Cf3fXX9+vUsXryYzZs3c/bsWd58803at29PeHg4cG+ca7Vq1bh58yapqalaJFhEnjnquZIiJzdY/f3vf6dz5854e3uzZcsWrl27Rp06ddixYwfx8fH4+/tz7do1ADw9PSlTpgwmk4mcnBwFKxF5ZuUuqH7/vdfcnvz09HRu375NYmIib775Jr6+vsybNw+A3bt3M3fuXC5duoSzs7OClYg8kxSupEhatGgR8+fPx9fXFzc3N/r168eXX35JSkpKnoD19ttvPzBNe244ExF5FtnY2JCcnMzu3bsBWLNmDb6+vmRmZlK1alVu3LhB8+bNad26NQsWLLC8Z27YsIGUlBRKlixpxepFRKxLnyKlSMi905orOzubGTNmEBwczNdff83AgQMJDw9n6dKlloAVFRWFq6ur5dFAERG5Z9SoUbzzzjtMnDiR7t2706tXL+zt7XnjjTfw8fHh559/plGjRpw7d46LFy8yatQoVqxYwaRJkyhdurS1yxcRsRqNuZJCL3dqYLh3h/Xy5ctER0fj5+dH165dLe0mTZrE0qVLCQwMpGfPnnh4eFj23T9OS0REoH79+vz4448EBQUxY8aMPPv69+/P999/z8WLF6lVqxY///wzq1ev1uyAIvLM08ASKdTuD1ajRo0iPDycqlWrkpiYSEZGBo0aNaJKlSoATJkyBRsbG8aPH0/FihXp1auX5TwKViIi92RkZGBnZ0dWVhZeXl6sXLmSVq1a0bx5c8t41MWLF3P48GHOnDmDh4cHVapUwc3NzcqVi4hYn3qupEiIi4tj+vTpfPjhhzRs2JBPP/2U5cuX4+Pjw9ChQ/H09LS0jYiIICAgAFtbWytWLCJSsNx/swogKyuLYsWK0bJlS5KSkli6dGmegHXnzh2KFy9urXJFRAokhSsp9FavXs3cuXNxcnJi48aNODo6AjBr1ixWrFhB8+bNGT58OJUrV85znNlsVsASEeHfwergwYPs37+fF154gdq1a1tm/GvZsiXHjh0jIiKC5s2bM2PGDKKjo9myZQsmkylPKBMReZbpsUAp9K5cuUJ6ejrJycmkpqZaQlRwcDAmk4nIyEhu3rxJWFgYFSpUsBynYCUico/JZCIqKgp/f39q1arFTz/9RIcOHejbty9t27Zl586dtG3bll69elG9enUSEhLYuXOnHqkWEfkVvStKofLrWQEBhgwZwkcffYSbmxtBQUEkJydb9n300Ue8/fbb5OTkUK5cuT+zVBGRAi/34ZULFy6wdu1a5s6dS2xsLOvWrSM1NZXPP/+crVu3ArBt2zbGjx9Pp06diImJ4dVXX7Vm6SIiBZIeC5RC4/4Z/WJiYjAMg6ysLJo0aQLA0qVLWbx4MRUrViQsLIyqVatajs195EWzAorIs+xh74EHDx7kiy++4OLFiyxcuNAyCdCuXbuYOnUqDg4OBAYG4uvrCzw4NktERP5NnzKl0Mj9QDBq1Cj8/f3x8/Pj3Xffxd/fn6tXrxIQEEBAQACXLl1i/PjxHD9+3HKsyWTCMAwFKxF5ptnY2HDu3Dnmzp1r2Xb8+HH27NnDwYMHOX36tGV7ixYtGDt2LDk5OYSFhbFr1y4ABSsRkUfQJ00pVD7//HMiIiJYtWoVW7duZf369ezdu5du3bqRmZlJ//796d27N0eOHGHFihV5jtUHAhF51pnNZubPn094eDgzZ84EoHfv3nzyySd4enoyf/58Dh8+bGmfOyFQuXLl8PLyslbZIiKFhh4LlELlgw8+wMHBgfDwcMu2s2fPUrduXQICAvj0008B2Lx5M76+vpq0QkTkVy5evMjMmTM5cOAAHTp0YOzYsQBERkYye/ZsatasyfDhw3nllVcsx9y+fZsSJUpYq2QRkUJDPVdSKBiGQU5ODidPnuTq1auW7Xfv3sXT05OJEyeyb98+y7533nkHW1tbzGaztUoWESmQPDw8GD16NK+++ipRUVGEhYUB0K1bN4KCgkhKSuLzzz8nJibGcoyClYjI41G4kgLp17MCmkwmbGxsCAgIIDo6mg0bNgDg4OBg+dPW1paSJUvmOU49VyIiD3Jzc2PcuHEPBKzu3bsTHBzM3r17WbJkCXfv3rVypSIihYvWuZIC5/7ZrHbv3s3Vq1fx9vbG3d2dli1bsnPnTubMmUN2djbvvfceqampbNmyhSpVqlC8eHErVy8iUjjkBqzQ0FCioqIAGDt2LP7+/tjZ2fHKK69YbmCJiMjj0ZgrKbBGjBjBkiVLsLGxwc7OjjFjxjBgwABOnDjBnDlz+Mc//kH58uUpXrw49vb2xMbGUqxYMU0TLCLyBFJSUggNDSUuLo5mzZrx8ccfW7skEZFCS+FKCoz7Q9GePXsYPXo0M2bMoFatWkyZMoXt27fTp08fhg0bhq2tLYmJiRw4cIAKFSrQqVMnbG1tyc7Oxs5OHbIiIk8iJSWFMWPGcOHCBSIjIylbtqy1SxIRKZQUrqTAWbZsGYcPH8bW1pY5c+ZYto8aNYqoqCj69OlD//79qVChQp7jzGazxliJiPxOly9fBnjgvVVERB6fbvFLgbNu3Tq+/vprmjVrxp07dyzjqKZPn47JZGLlypWkpaUxcuRIXFxcLMcpWImI/H4KVSIif5xmCxSreljH6aZNmxgwYADHjh1j+fLl3Lp1y7Jv2rRpNGnShHPnzuHs7PxnlioiIiIi8kh6LFCs5v5ZAc+fP4+dnR02NjaWu6c9evQgLi6OkSNH0rVr1zzTrOeOz9LkFSIiIiJSUKjnSqzCMAxLsJo4cSJ+fn7UqVMHf39/Zs6cCcDKlSupV68eM2fOZM2aNaSlpVmOV7ASERERkYJGY67EKnJD0ccff0x4eDgRERGkpaVx7NgxJk6cyPXr1wkLC2PVqlX06dOHoKAgypUrx9tvv/3AOURERERECgKFK7GaX375hd27dzN16lQ6duwIQHp6OlWqVCEoKIgaNWrQp08fli1bRkhICG3btrVuwSIiIiIij6DHAuVP8+vhfWazmcTERFJTUy3bSpYsyXvvvUfLli05fPgwZrMZgAkTJmBra2t5LSIiIiJS0ChcyZ8iJyfH8hjfuXPnSE9Pp0yZMrz77rscPnyY48ePW9o6Ozvz3HPPcfbs2QemV9d06yIiIiJSUClcyVN3/6yAkydPJigoiL179wLg4+PD0aNHiYiI4OjRowCkpaWRnJxM1apVrVaziIiIiMiT0lTs8qcZN24cCxcuZNGiRXh7e1umXF+4cCHh4eEYhoGbmxs3b97k9u3bxMXFYWdnp1kBRURERKRQULiSp8psNmNra8uRI0fw9/cnPDwcHx8fIG+PVnR0NImJifzwww9UrVqV4OBg7OzsyM7Oxs5O866IiIiISMGnT63yVBw6dIiGDRtaxkjduXOHtLQ0PDw8LG1yg1V2djYNGzakSZMmec5hNpsVrERERESk0NCYK8l3X3zxBR06dLCMoQK4e/cuV65cISMjA4CsrCzLvujoaLZt20ZmZmae82jyChEREREpTBSuJF8tXLiQwMBAwsPDqVGjhmX7W2+9RevWrQkICOD06dMUK1YMuBe6wsLCOHjwIPb29tYqW0RERETkD9OYK8k3CxYsYPDgwaxevRo/Pz/L9oMHD9K4cWN27tzJzJkzSUpKYtKkSaSlpbF161ZSUlL44Ycf9AigiIiIiBRqCleSLzZu3Iifnx+bNm2iffv2lu3vvvsuzs7OLFmyBFtbWxISEliwYAFbt27F3d2dF154gaVLl1KsWDHL5BciIiIiIoWRugrkD7t79y7bt2+natWqnD592rK9S5cunDhxgq1bt1pCU506dQgPD+fKlSs899xz2NraYjKZNCugiIiIiBR6+jQrf5iDgwMTJ07EwcGBVatWYRgG0dHRnDhxgs2bN1OlShXLWlW5f5YvX95yvGEYClYiIiIiUujpsUDJNykpKYSGhrJlyxZu3rxJQkICHh4eZGVlWSawePvtt6lbty5hYWFWrlZEREREJH8pXEm+unz5MmFhYezfv59u3boRHBwM3FuzqkOHDpw8eZLExERL2BIRERERKSo0FbvkqwoVKjBmzBi8vb1Zs2YNs2bNAsDPz4/k5GRLsMrOzrZypSIiIiIi+Us9V/JUpKSkEBYWxuHDhzl58iQuLi55gpXGWImIiIhIUaNwJU9NSkoKo0aNIjU1lU2bNilYiYiIiEiRpnAlT9X169dxdnbGxsZGwUpEREREijSFK/lT5OTkYGOjIX4iIiIiUnQpXImIiIiIiOQDdSWIiIiIiIjkA4UrERERERGRfKBwJSIiIiIikg8UrkRERERERPKBwpWIiIiIiEg+ULgSERERERHJBwpXIiIif6LJkydTr169JzrGZDKxcePG39x/5swZTCYT8fHxf6g2ERH5YxSuRETksQUEBGAymR74atu27WOfY/fu3ZhMJm7cuPH0Cv0DDh8+jMlk4sCBAw/d7+Pjg5+f3+8+f3BwMN9+++3vPl5ERAouO2sXICIihUvbtm1ZsmRJnm0ODg75/n0yMzOxt7fP9/P+Jw0aNKBu3bosXryY1157Lc++M2fO8N133/H1118/8XkNw8BsNuPk5ISTk1N+lSsiIgWIeq5EROSJODg44ObmluerTJkylv0mk4kvv/ySTp06UaJECby8vIiKigLuhZPmzZsDUKZMGUwmEwEBAQA0a9aMwYMHM3z4cFxdXWnTpg0Ae/bsoVGjRjg4OODu7s7o0aPJzs62fL/c4wYPHoyzszOurq5MmDABwzAA+Pvf/06tWrUeuI569eoxYcKEh17j+++/z+rVq7l9+3ae7UuXLsXd3Z22bduyfPlyGjZsSKlSpXBzc6NHjx5cuXLF0ja3h+6bb76hQYMGODg4EB0d/cBjgbGxsbRq1QpXV1ecnZ1p2rQpP/zwwwM1Xbp0CV9fXxwdHalatSpr1679zb8jgMTERHx9fXFycqJChQr07t2bq1evWvavXbuW2rVr4+joSNmyZWnZsiXp6emPPKeIiDyawpWIiOS7KVOm0LVrVxISEmjXrh09e/bk2rVrVKpUiXXr1gFw7NgxLl26xGeffWY57quvvsLe3p79+/fzxRdfcPHiRdq1a8err77KkSNH+O///m8iIiL4+OOP83y/r776Cjs7O2JiYvjss8+YM2cOX375JQD9+/cnKSmJ2NhYS/u4uDgSEhLo16/fQ+vv2bMnd+/ezRNgDMPgq6++IiAgAFtbW7KysggJCeHIkSNs3LiRM2fOWILi/UaPHs20adNISkqiTp06D+xPS0ujb9++REdHc+DAAby8vGjXrh1paWl52k2YMIHOnTtz5MgRevbsSbdu3UhKSnpo/Tdu3KBFixbUr1+fQ4cOsW3bNi5fvkzXrl2Be0Gte/fulp/N7t278fPzswRSERH5nQwREZHH1LdvX8PW1tYoWbJknq/Q0FBLG8AYP3685fWtW7cMwPjmm28MwzCM7777zgCM69ev5zl306ZNjfr16+fZNnbsWKN69epGTk6OZVt4eLjh5ORkmM1my3E1a9bM02bUqFFGzZo1La99fX2N//qv/7K8HjJkiNGsWbNHXmu3bt2Mpk2bWl5/++23BmCcOHHioe1jY2MNwEhLS8tznRs3bszTbtKkSUbdunV/8/uazWajVKlSxtdff23ZBhgDBw7M065x48aWazp9+rQBGHFxcYZhGEZISIjRunXrPO3Pnz9vAMaxY8eMw4cPG4Bx5syZR/4MRETkyajnSkREnkjz5s2Jj4/P8zVw4MA8be7voSlZsiSlS5fO88jcb2nQoEGe10lJSXh7e2MymSzb3njjDW7dusWFCxcs21577bU8bby9vTlx4gRmsxmAv/71r6xatYo7d+6QmZnJypUr6d+//yNr6d+/P3v37iU5ORmAxYsX07RpU6pVqwbcm/iiffv2VK5cmVKlStG0aVMAzp07l+c8DRs2fOT3uXz5Mn/961/x8vLC2dmZ0qVLc+vWrQfO4+3t/cDr3+q5OnLkCN99951lfJeTkxM1atQAIDk5mbp16+Lj40Pt2rV57733WLRoEdevX39knSIi8p9pQgsREXkiJUuWtASM31KsWLE8r00mEzk5OY917qehffv2ODg4sGHDBuzt7cnKyqJLly6PPMbHx4fKlSuzdOlSRowYwfr161mwYAEA6enptGnThjZt2rBixQrKlSvHuXPnaNOmDZmZmU90TX379uXnn3/ms88+w9PTEwcHB7y9vR84z5O4desW7du3Z/r06Q/sc3d3x9bWln/+8598//337Nixg3nz5jFu3DgOHjzICy+88Lu/r4jIs07hSkRE/lS5MwDm9io9Ss2aNVm3bh2GYVh6pvbv30+pUqV4/vnnLe0OHjyY57jcsUu2trYA2NnZ0bdvX5YsWYK9vT3dunXD0dHxkd/bxsaGfv36ERERgYeHB/b29pZAdvToUX7++WemTZtGpUqVADh06NBj/gTy2r9/P/Pnz6ddu3YAnD9/Ps/EE/dfU58+ffK8rl+//kPP+corr7Bu3TqqVKmCnd3Df9WbTCbeeOMN3njjDSZOnIinpycbNmwgKCjod12HiIhoQgsREXlCd+/eJSUlJc/Xw8LAb/H09MRkMrF582ZSU1O5devWb7YdNGgQ58+fZ8iQIRw9epRNmzYxadIkgoKCsLH596+wc+fOERQUxLFjx1i1ahXz5s1j2LBhec71wQcfsGvXLrZt2/YfHwnM1a9fPy5evMjYsWPp3r27JZBVrlwZe3t75s2bx6lTp4iKiiIkJOSxfwb38/LyYvny5SQlJXHw4EF69uz50OC3Zs0aFi9ezPHjx5k0aRIxMTEMHjz4oecMDAzk2rVrdO/endjYWJKTk9m+fTv9+vXDbDZz8OBBwsLCOHToEOfOnWP9+vWkpqZSs2bN33UNIiJyj8KViIg8kW3btuHu7p7nq0mTJo99vIeHB1OmTGH06NFUqFDhNwNCbtutW7cSExND3bp1GThwIO+//z7jx4/P065Pnz5kZGTQqFEjAgMDGTZsGAMGDMjTxsvLi9dff50aNWrQuHHjx6q1cuXKtGzZkuvXr+cJZOXKlWPp0qWsWbOGl156iWnTpjFr1qzH/hncLyIiguvXr/PKK6/Qu3dvhg4dSvny5R9oN2XKFCIjI6lTpw7Lli1j1apVvPTSSw89Z8WKFdm/fz9ms5nWrVtTu3Zthg8fjouLCzY2NpQuXZq9e/fSrl07/vKXvzB+/Hhmz56Nr6/v77oGERG5x2QYmndVREQKr2bNmlGvXj0+/fTTR7YzDAMvLy8GDRqkR99EROSp0JgrEREp8lJTU4mMjCQlJeU317YSERH5oxSuRESkyCtfvjyurq4sXLiQMmXKWLscEREpovRYoIiIiIiISD7QhBYiIiIiIiL5QOFKREREREQkHyhciYiIiIiI5AOFKxERERERkXygcCUiIiIiIpIPFK5ERERERETygcKViIiIiIhIPlC4EhERERERyQf/D1uCgRJCDLZfAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["import openpyxl"],"metadata":{"id":"7g9Du_tmEVqs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data.to_excel(\"Final_Data_GBPUSD_EPU.xlsx\")\n","data.to_excel('/content/drive/MyDrive/Final_Data_GBPUSD_EPU.xlsx', index=False)"],"metadata":{"id":"FZGh9Kjo7ZmW"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Descriptive Statistics of final, cleaned variables"],"metadata":{"id":"zfUjQZcvFLBu"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1711360730668,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"T62mwWOW75Gi","outputId":"e9f579a2-a3c7-40ac-8052-451f5f78d1f6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["           GBPUSD      CPI_US      CPI_UK  Money Market Rate_US  \\\n","count  211.000000  211.000000  211.000000            211.000000   \n","mean    -0.000025    0.001993    0.002132              0.000132   \n","std      0.018034    0.001986    0.002754              0.000515   \n","min     -0.047321   -0.003498   -0.005413             -0.001200   \n","25%     -0.011570    0.000628    0.000550             -0.000100   \n","50%     -0.000200    0.001868    0.002444              0.000000   \n","75%      0.011091    0.003158    0.003680              0.000250   \n","max      0.048292    0.007116    0.008622              0.001600   \n","\n","       Money Market Rate_UK      IPI_US      IPI_UK       M2_US       M2_UK  \\\n","count            211.000000  211.000000  211.000000  211.000000  211.000000   \n","mean               0.000029    0.001135   -0.000228    0.004763    0.004516   \n","std                0.000392    0.004744    0.006531    0.002507    0.020928   \n","min               -0.001100   -0.012357   -0.019083   -0.001520   -0.076999   \n","25%               -0.000100   -0.002196   -0.004035    0.003272   -0.005602   \n","50%                0.000000    0.001216   -0.000880    0.004809    0.004724   \n","75%                0.000100    0.004179    0.003908    0.006139    0.016470   \n","max                0.001100    0.012445    0.017874    0.011447    0.066326   \n","\n","       Stock Market News  Economic Development News    FED News  \\\n","count         211.000000                 211.000000  211.000000   \n","mean           -0.000324                  -0.000152   -0.000082   \n","std             0.006443                   0.011207    0.011065   \n","min            -0.018599                  -0.030415   -0.024708   \n","25%            -0.003852                  -0.008206   -0.008092   \n","50%            -0.000003                  -0.001099   -0.000859   \n","75%             0.003779                   0.006298    0.007708   \n","max             0.014545                   0.027623    0.024983   \n","\n","       Micro Finance News  International Trade News      EPU_US      EPU_UK  \n","count          211.000000                211.000000  211.000000  211.000000  \n","mean             0.000688                 -0.000429    0.012683    0.000352  \n","std              0.025268                  0.010439    0.550068    0.289911  \n","min             -0.064905                 -0.027713   -1.253588   -0.656585  \n","25%             -0.016109                 -0.007946   -0.415050   -0.203735  \n","50%              0.005232                 -0.000119    0.077612    0.020484  \n","75%              0.015995                  0.006500    0.355336    0.209855  \n","max              0.056793                  0.027952    1.502079    0.753851  "],"text/html":["\n","  <div id=\"df-02d51f54-504d-495f-8472-ce6c038dc30c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GBPUSD</th>\n","      <th>CPI_US</th>\n","      <th>CPI_UK</th>\n","      <th>Money Market Rate_US</th>\n","      <th>Money Market Rate_UK</th>\n","      <th>IPI_US</th>\n","      <th>IPI_UK</th>\n","      <th>M2_US</th>\n","      <th>M2_UK</th>\n","      <th>Stock Market News</th>\n","      <th>Economic Development News</th>\n","      <th>FED News</th>\n","      <th>Micro Finance News</th>\n","      <th>International Trade News</th>\n","      <th>EPU_US</th>\n","      <th>EPU_UK</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","      <td>211.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>-0.000025</td>\n","      <td>0.001993</td>\n","      <td>0.002132</td>\n","      <td>0.000132</td>\n","      <td>0.000029</td>\n","      <td>0.001135</td>\n","      <td>-0.000228</td>\n","      <td>0.004763</td>\n","      <td>0.004516</td>\n","      <td>-0.000324</td>\n","      <td>-0.000152</td>\n","      <td>-0.000082</td>\n","      <td>0.000688</td>\n","      <td>-0.000429</td>\n","      <td>0.012683</td>\n","      <td>0.000352</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.018034</td>\n","      <td>0.001986</td>\n","      <td>0.002754</td>\n","      <td>0.000515</td>\n","      <td>0.000392</td>\n","      <td>0.004744</td>\n","      <td>0.006531</td>\n","      <td>0.002507</td>\n","      <td>0.020928</td>\n","      <td>0.006443</td>\n","      <td>0.011207</td>\n","      <td>0.011065</td>\n","      <td>0.025268</td>\n","      <td>0.010439</td>\n","      <td>0.550068</td>\n","      <td>0.289911</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-0.047321</td>\n","      <td>-0.003498</td>\n","      <td>-0.005413</td>\n","      <td>-0.001200</td>\n","      <td>-0.001100</td>\n","      <td>-0.012357</td>\n","      <td>-0.019083</td>\n","      <td>-0.001520</td>\n","      <td>-0.076999</td>\n","      <td>-0.018599</td>\n","      <td>-0.030415</td>\n","      <td>-0.024708</td>\n","      <td>-0.064905</td>\n","      <td>-0.027713</td>\n","      <td>-1.253588</td>\n","      <td>-0.656585</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-0.011570</td>\n","      <td>0.000628</td>\n","      <td>0.000550</td>\n","      <td>-0.000100</td>\n","      <td>-0.000100</td>\n","      <td>-0.002196</td>\n","      <td>-0.004035</td>\n","      <td>0.003272</td>\n","      <td>-0.005602</td>\n","      <td>-0.003852</td>\n","      <td>-0.008206</td>\n","      <td>-0.008092</td>\n","      <td>-0.016109</td>\n","      <td>-0.007946</td>\n","      <td>-0.415050</td>\n","      <td>-0.203735</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>-0.000200</td>\n","      <td>0.001868</td>\n","      <td>0.002444</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.001216</td>\n","      <td>-0.000880</td>\n","      <td>0.004809</td>\n","      <td>0.004724</td>\n","      <td>-0.000003</td>\n","      <td>-0.001099</td>\n","      <td>-0.000859</td>\n","      <td>0.005232</td>\n","      <td>-0.000119</td>\n","      <td>0.077612</td>\n","      <td>0.020484</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.011091</td>\n","      <td>0.003158</td>\n","      <td>0.003680</td>\n","      <td>0.000250</td>\n","      <td>0.000100</td>\n","      <td>0.004179</td>\n","      <td>0.003908</td>\n","      <td>0.006139</td>\n","      <td>0.016470</td>\n","      <td>0.003779</td>\n","      <td>0.006298</td>\n","      <td>0.007708</td>\n","      <td>0.015995</td>\n","      <td>0.006500</td>\n","      <td>0.355336</td>\n","      <td>0.209855</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.048292</td>\n","      <td>0.007116</td>\n","      <td>0.008622</td>\n","      <td>0.001600</td>\n","      <td>0.001100</td>\n","      <td>0.012445</td>\n","      <td>0.017874</td>\n","      <td>0.011447</td>\n","      <td>0.066326</td>\n","      <td>0.014545</td>\n","      <td>0.027623</td>\n","      <td>0.024983</td>\n","      <td>0.056793</td>\n","      <td>0.027952</td>\n","      <td>1.502079</td>\n","      <td>0.753851</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02d51f54-504d-495f-8472-ce6c038dc30c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-02d51f54-504d-495f-8472-ce6c038dc30c button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-02d51f54-504d-495f-8472-ce6c038dc30c');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-8d3ffcd0-3dea-4df4-9910-d34984acfdc3\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d3ffcd0-3dea-4df4-9910-d34984acfdc3')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-8d3ffcd0-3dea-4df4-9910-d34984acfdc3 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"GBPUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59884600367394,\n        \"min\": -0.047321236497454955,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -2.534014663986239e-05,\n          -0.00020031549489751832,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59909622970564,\n        \"min\": -0.0034980433127662636,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.001993128693970775,\n          0.0018675086613582081,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59901956756417,\n        \"min\": -0.005412838244263085,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0021315383155672573,\n          0.0024443276748478127,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59970493518775,\n        \"min\": -0.0011999999999999997,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.00013222748815165894,\n          0.0,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Money Market Rate_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.5997441571865,\n        \"min\": -0.0011000000000000038,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.8909952606635024e-05,\n          0.0,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59930280697534,\n        \"min\": -0.0123574544234204,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0011346083935949722,\n          0.0012156792004689443,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPI_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59955969638185,\n        \"min\": -0.019082814416440996,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.00022765584219853273,\n          -0.0008798944694392574,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.598178698191,\n        \"min\": -0.001519631616664796,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0047626141931012295,\n          0.0048089066046062,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M2_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59824235419995,\n        \"min\": -0.07699919034517144,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.004515769362606691,\n          0.004724158836422632,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Market News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59966561263202,\n        \"min\": -0.01859941445383617,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.00032407336912716067,\n          -2.724757988126214e-06,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Economic Development News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59950183118238,\n        \"min\": -0.030415123871464278,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.00015191222564859034,\n          -0.0010988876076798437,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FED News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59926098596587,\n        \"min\": -0.02470761491829343,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -8.216885827626328e-05,\n          -0.0008590248979668536,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Micro Finance News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59861385503314,\n        \"min\": -0.06490524626209426,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0006883086541187695,\n          0.005231804533153084,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"International Trade News\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.59932851582867,\n        \"min\": -0.027712976353242214,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.00042913062387422486,\n          -0.00011931463341618986,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_US\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.5620421111517,\n        \"min\": -1.2535877108101587,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.012683250814253413,\n          0.07761248556810108,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EPU_UK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.57994504289381,\n        \"min\": -0.6565847190764948,\n        \"max\": 211.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0003521478615731176,\n          0.020484297298255516,\n          211.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":30}],"source":[" data.describe()"]},{"cell_type":"markdown","source":["# Johansen test (just for having a look)"],"metadata":{"id":"x8MTGOWeqbts"}},{"cell_type":"code","source":["data= pd.read_excel('/content/drive/MyDrive/Final_Data_GBPUSD_EPU.xlsx')"],"metadata":{"id":"ONo-u5L93WZ6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","from statsmodels.tsa.stattools import coint\n","result_coint = coint(data['GBPUSD'], data['Stock Market News'])\n","result_coint = coint(data['GBPUSD'], data['Economic Development News'])\n","result_coint = coint(data['GBPUSD'], data['FED News'])\n","result_coint = coint(data['GBPUSD'], data['M2_US'])\n","result_coint = coint(data['GBPUSD'], data['EPU_US'])\n","result_coint = coint(data['GBPUSD'], data['EPU_UK'])\n","# Extract the p-value from the test result\n","p_value = result_coint[1]\n","print(p_value)\n"],"metadata":{"id":"cVqjpAVnqak8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711360730669,"user_tz":-60,"elapsed":8,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"af226ae3-5afe-4a3c-8198-36764a44bce1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2.293174483296647e-17\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import statsmodels.api as sm\n","from statsmodels.tsa.vector_ar.vecm import coint_johansen\n","\n","# Assuming 'data' is your DataFrame containing the time series data\n","\n","# Step 1: Perform Johansen cointegration test\n","def johansen_cointegration_test(data):\n","    result = coint_johansen(data, det_order=0, k_ar_diff=1)\n","    print(\"Eigenvalues:\", result.eig)\n","    print(\"Trace Statistic:\", result.lr1)\n","    print(\"Critical Values (90%, 95%, 99%):\", result.cvt)\n","\n","    # Get the number of cointegrating vectors (rank) using maximum eigenvalue test\n","    rank_max_eig = result.ind\n","    print(\"Rank (using max eigenvalue test):\", rank_max_eig)\n","\n","    # Get the number of cointegrating vectors (rank) using trace test\n","    rank_trace = result.lr2\n","    print(\"Rank (using trace test):\", rank_trace)\n","\n","    return result\n","\n","johansen_result = johansen_cointegration_test(data)\n"],"metadata":{"id":"YnoIIuwmuCpb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711360731003,"user_tz":-60,"elapsed":341,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"14aa01fe-f378-4adf-a0b5-94fc81112d0e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Eigenvalues: [0.76701646 0.60776339 0.59730659 0.59167796 0.54898521 0.53662789\n"," 0.49099506 0.44320719 0.42532001 0.37957449 0.32713111 0.30546866\n"," 0.29531108 0.24037725 0.17010984 0.13956773]\n","Trace Statistic: [2043.60720838 1739.13862864 1543.53761089 1353.43543929 1166.23432577\n","  999.81699857  839.04900566  697.91181618  575.52934238  459.75547999\n","  359.98938515  277.18258658  200.99832622  127.8485712    70.38750182\n","   31.41695891]\n","Critical Values (90%, 95%, 99%): [[     nan      nan      nan]\n"," [     nan      nan      nan]\n"," [     nan      nan      nan]\n"," [     nan      nan      nan]\n"," [326.5354 334.9795 351.215 ]\n"," [277.374  285.1402 300.2821]\n"," [232.103  239.2468 253.2526]\n"," [190.8714 197.3772 210.0366]\n"," [153.6341 159.529  171.0905]\n"," [120.3673 125.6185 135.9825]\n"," [ 91.109   95.7542 104.9637]\n"," [ 65.8202  69.8189  77.8202]\n"," [ 44.4929  47.8545  54.6815]\n"," [ 27.0669  29.7961  35.4628]\n"," [ 13.4294  15.4943  19.9349]\n"," [  2.7055   3.8415   6.6349]]\n","Rank (using max eigenvalue test): [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]\n","Rank (using trace test): [304.46857974 195.60101775 190.1021716  187.20111352 166.4173272\n"," 160.76799291 141.13718948 122.3824738  115.77386239  99.76609484\n","  82.80679857  76.18426036  73.14975502  57.46106938  38.97054291\n","  31.41695891]\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-33-7449a5313a5b>:10: HypothesisTestWarning: Critical values are only available for time series with 12 variables at most.\n","  result = coint_johansen(data, det_order=0, k_ar_diff=1)\n"]}]},{"cell_type":"code","source":["# Step 2: Compute residuals\n","def compute_residuals(data, johansen_result):\n","    # Get the cointegrating vectors from the result and reshape if necessary\n","    cointegrating_vectors = johansen_result.evec\n","    if len(cointegrating_vectors.shape) == 1:\n","        cointegrating_vectors = cointegrating_vectors.reshape(-1, 1)\n","    #print(cointegrating_vectors)\n","    # Select the relevant cointegrating vectors based on the number of dimensions\n","    cointegrating_vectors = cointegrating_vectors[:, :16] #johansen_result.ind.max()\n","\n","    # Project the data onto the cointegrating space\n","    residuals = data.values - np.dot(data.values, cointegrating_vectors)\n","\n","    return pd.DataFrame(residuals, index=data.index, columns=data.columns)\n","\n","residuals = compute_residuals(data, johansen_result)\n","\n","\n","# Step 3: Check residual stationarity\n","def check_residual_stationarity(residuals):\n","    adf_results = {}\n","    for column in residuals.columns:\n","        adf_result = sm.tsa.adfuller(residuals[column])\n","        adf_results[column] = adf_result\n","\n","    return adf_results\n","\n","adf_results = check_residual_stationarity(residuals)\n","\n","# Step 4: Print ADF test results\n","for column, result in adf_results.items():\n","    print(f\"ADF test results for {column}:\")\n","    print(\"ADF Statistic:\", result[0])\n","    print(\"P-value:\", result[1])\n","    print(\"Critical Values:\", result[4])\n","    print(\"Is stationary:\", result[0] < result[4]['5%'])  # Assuming 5% significance level\n","    print()"],"metadata":{"id":"rwemZve13p7o","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711360731003,"user_tz":-60,"elapsed":4,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"b1ae362a-95f3-4419-de51-265284dca83c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["ADF test results for GBPUSD:\n","ADF Statistic: -4.38427958974662\n","P-value: 0.0003164362272091202\n","Critical Values: {'1%': -3.4638151713286316, '5%': -2.876250632135043, '10%': -2.574611347821651}\n","Is stationary: True\n","\n","ADF test results for CPI_US:\n","ADF Statistic: -9.61190392038015\n","P-value: 1.802355240760517e-16\n","Critical Values: {'1%': -3.4624988216864776, '5%': -2.8756749365852587, '10%': -2.5743041549627677}\n","Is stationary: True\n","\n","ADF test results for CPI_UK:\n","ADF Statistic: -7.534613191873943\n","P-value: 3.505113171411259e-11\n","Critical Values: {'1%': -3.462980134086401, '5%': -2.875885461947131, '10%': -2.5744164898444515}\n","Is stationary: True\n","\n","ADF test results for Money Market Rate_US:\n","ADF Statistic: -10.988319520311942\n","P-value: 7.165072240347004e-20\n","Critical Values: {'1%': -3.4623415245233145, '5%': -2.875606128263243, '10%': -2.574267439846904}\n","Is stationary: True\n","\n","ADF test results for Money Market Rate_UK:\n","ADF Statistic: -14.888048772790956\n","P-value: 1.5725736575671853e-27\n","Critical Values: {'1%': -3.4620315036789666, '5%': -2.8754705024827127, '10%': -2.5741950726860647}\n","Is stationary: True\n","\n","ADF test results for IPI_US:\n","ADF Statistic: -15.257212616002839\n","P-value: 4.916676227623278e-28\n","Critical Values: {'1%': -3.4620315036789666, '5%': -2.8754705024827127, '10%': -2.5741950726860647}\n","Is stationary: True\n","\n","ADF test results for IPI_UK:\n","ADF Statistic: -14.61898130272112\n","P-value: 3.9125516201789485e-27\n","Critical Values: {'1%': -3.4620315036789666, '5%': -2.8754705024827127, '10%': -2.5741950726860647}\n","Is stationary: True\n","\n","ADF test results for M2_US:\n","ADF Statistic: -17.755007774651876\n","P-value: 3.357344311832477e-30\n","Critical Values: {'1%': -3.461878735881654, '5%': -2.875403665910809, '10%': -2.574159410430839}\n","Is stationary: True\n","\n","ADF test results for M2_UK:\n","ADF Statistic: -3.6333234147123625\n","P-value: 0.005154998060964292\n","Critical Values: {'1%': -3.4638151713286316, '5%': -2.876250632135043, '10%': -2.574611347821651}\n","Is stationary: True\n","\n","ADF test results for Stock Market News:\n","ADF Statistic: -15.021273837011863\n","P-value: 1.0214863695929164e-27\n","Critical Values: {'1%': -3.461878735881654, '5%': -2.875403665910809, '10%': -2.574159410430839}\n","Is stationary: True\n","\n","ADF test results for Economic Development News:\n","ADF Statistic: -15.573287404134977\n","P-value: 1.974292581142657e-28\n","Critical Values: {'1%': -3.461878735881654, '5%': -2.875403665910809, '10%': -2.574159410430839}\n","Is stationary: True\n","\n","ADF test results for FED News:\n","ADF Statistic: -2.099471908419286\n","P-value: 0.2447270027989354\n","Critical Values: {'1%': -3.4636447617687436, '5%': -2.8761761179270766, '10%': -2.57457158581854}\n","Is stationary: False\n","\n","ADF test results for Micro Finance News:\n","ADF Statistic: -13.904688407242359\n","P-value: 5.651033949461904e-26\n","Critical Values: {'1%': -3.461878735881654, '5%': -2.875403665910809, '10%': -2.574159410430839}\n","Is stationary: True\n","\n","ADF test results for International Trade News:\n","ADF Statistic: -7.405857817192627\n","P-value: 7.348262119351457e-11\n","Critical Values: {'1%': -3.4621857592784546, '5%': -2.875537986778846, '10%': -2.574231080806213}\n","Is stationary: True\n","\n","ADF test results for EPU_US:\n","ADF Statistic: -7.330236718285904\n","P-value: 1.1328800604806662e-10\n","Critical Values: {'1%': -3.4620315036789666, '5%': -2.8754705024827127, '10%': -2.5741950726860647}\n","Is stationary: True\n","\n","ADF test results for EPU_UK:\n","ADF Statistic: -5.827321559831287\n","P-value: 4.0506847023087775e-07\n","Critical Values: {'1%': -3.4620315036789666, '5%': -2.8754705024827127, '10%': -2.5741950726860647}\n","Is stationary: True\n","\n"]}]},{"cell_type":"code","source":["#T-Y wald test\n","\n","import numpy as np\n","import statsmodels.api as sm\n","\n","# Assuming 'data' is your DataFrame\n","dependent_variable = data.iloc[:, 0]  # Extract dependent variable (first column)\n","independent_variables = data.iloc[:, 1:16]  # Extract independent variables (remaining columns)\n","\n","# Fit the multivariate OLS model\n","model = sm.OLS(dependent_variable, independent_variables).fit()\n","\n","# Define the null hypothesis matrix 'R' and test whether the coefficients sum to zero\n","R = np.eye(len(model.params))  # Assuming you want to test each coefficient separately\n","wald_test = model.wald_test(R)\n","\n","# Print the Wald test results\n","print(wald_test)\n","# very low p-value. These results suggest that there is strong evidence to reject the null hypothesis, indicating that the coefficients of the variables in the model are jointly not equal to zero. In other words, there is statistical evidence to support the presence of at least one significant relationship among the variables included in the model."],"metadata":{"id":"d9VuJWce28Dn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711360731003,"user_tz":-60,"elapsed":2,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"2cd4a2f5-5280-4054-c85b-f7bf855a7226"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<F test: F=array([[5.66021091]]), p=1.4144484350923338e-09, df_denom=196, df_num=15>\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:1914: FutureWarning: The behavior of wald_test will change after 0.14 to returning scalar test statistic values. To get the future behavior now, set scalar to True. To silence this message while retaining the legacy behavior, set scalar to False.\n","  warnings.warn(\n"]}]},{"cell_type":"code","source":["# #we do not need this, as we do not go for VECM model\n","\n","# import numpy as np\n","# import pandas as pd\n","# from statsmodels.tsa.vector_ar.vecm import coint_johansen\n","# from statsmodels.tsa.vector_ar.vecm import VECM\n","# from statsmodels.tsa.vector_ar.vecm import select_order\n","\n","# # Assuming you have a DataFrame 'data' containing your variables\n","\n","# # Step 1: Johansen cointegration test\n","# johansen_results = coint_johansen(data, det_order=0, k_ar_diff=1)\n","\n","# # Extract the number of cointegrating vectors\n","# num_coint_vectors = np.linalg.matrix_rank(johansen_results.eig)\n","\n","# lag_order = select_order(data, maxlags=10)\n","\n","# # Step 2: Estimate VECM\n","# model = VECM(data, k_ar_diff=6) #lag_order\n","# vecm_results = model.fit()\n","\n","# # Get impulse response functions\n","# irf = vecm_results.irf(10)  # Adjust the horizon (10 in this example)\n","\n","# # Extract cointegrating vectors\n","# coint_vectors = vecm_results.alpha\n","\n","# # Construct VAR representation\n","# A = vecm_results.alpha\n","# B = vecm_results.beta\n","# var_coefficients = np.dot(np.linalg.inv(np.eye(data.shape[1]) - A - B), coint_vectors)\n","\n","# # Create VAR model\n","# model_var = VAR(data)\n","# result_var = model_var.fit(var_coefficients, maxlags=6, ic='aic')\n","\n","# # Compute FEVD for VAR model\n","# fevd_var = result_var.fevd(10)  # Adjust the horizon (10 in this example)\n","\n","# # Print or use the results as needed\n","# print(fevd_var.summary())\n","# irf = vecm_results.irf(10)\n","# irf.plot(0)"],"metadata":{"id":"xHisyGKVBWDU"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# VAR model"],"metadata":{"id":"5f8f1wqDFQkR"}},{"cell_type":"code","source":["#SVAR\n","#n_vars = 16\n","#A = np.eye(n_vars)\n","#A\n","#svar_model = sm.tsa.SVAR(data, A = A, svar_type='A')\n","#results = svar_model.fit(maxlags=7)  # Example with maximum lag order of 2\n","#results.summary()"],"metadata":{"id":"RRk9hHNlTocc"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1711360731330,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"OM0aPM6QTDzq","outputId":"84fe9c44-1577-44ba-f75d-7f4fc4130611"},"outputs":[{"output_type":"stream","name":"stdout","text":["GBPUSD\n","3.19588597468977e-18\n","CPI_US\n","3.502231941052436e-07\n","CPI_UK\n","0.0865823815883699\n","Money Market Rate_US\n","0.00700767055173491\n","Money Market Rate_UK\n","2.777539860806871e-10\n","IPI_US\n","2.6210326170883224e-07\n","IPI_UK\n","2.153643129942898e-30\n","M2_US\n","1.3605200464959124e-12\n","M2_UK\n","7.474982369231338e-07\n","Stock Market News\n","8.493930772607955e-25\n","Economic Development News\n","0.0001643368490007072\n","FED News\n","0.0004392427636832819\n","Micro Finance News\n","0.0056028604031803965\n","International Trade News\n","0.0006121354259344661\n","EPU_US\n","3.362092877603312e-17\n","EPU_UK\n","1.5692845900488796e-20\n"]}],"source":["# Check if time series are normal after first differencing\n","from statsmodels.tsa.stattools import adfuller\n","for column in data:\n","  print(column)\n","  print(adfuller(data[column])[1])\n","  #P value is always <0.1-> stationary"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zRLizG4y4mqy"},"outputs":[],"source":["import pandas as pd\n","data.to_excel(\"output.xlsx\", sheet_name = 'RadyGBPUSD')\n","#data.to_csv('output.csv', index = False)\n","data.to_csv('GBPUSD_data.csv')\n","#data\n","with open('GBPUSD_data.csv', 'w') as f:\n","  data.to_csv(f)\n","data.to_excel('GBPUSD_data.xlsx', index=False)\n","data.to_pickle(\"GBPUSD.pkl\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qj2W9X8s8HgY"},"outputs":[],"source":["# libraries for VAR\n","%matplotlib inline\n","import numpy as np\n","import pandas as pd\n","import scipy\n","from datetime import datetime, timedelta\n","import statsmodels.api as sm\n","from statsmodels.tsa.api import VAR\n","import matplotlib.pyplot as plt\n","import pandas_datareader as pdr"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":240},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1711360731647,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"pRWMWymQ8knA","outputId":"8a4f3310-8793-4ae3-e6c7-b6022a31b77b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.iolib.table.SimpleTable'>"],"text/html":["<table class=\"simpletable\">\n","<caption>VAR Order Selection (* highlights the minimums)</caption>\n","<tr>\n","  <td></td>      <th>AIC</th>         <th>BIC</th>         <th>FPE</th>        <th>HQIC</th>    \n","</tr>\n","<tr>\n","  <th>0</th> <td>    -154.4</td>  <td>    -154.2*</td> <td> 8.457e-68</td>  <td>    -154.3*</td>\n","</tr>\n","<tr>\n","  <th>1</th> <td>    -155.0*</td> <td>    -150.6</td>  <td> 4.711e-68*</td> <td>    -153.2</td> \n","</tr>\n","<tr>\n","  <th>2</th> <td>    -154.8</td>  <td>    -146.2</td>  <td> 6.312e-68</td>  <td>    -151.3</td> \n","</tr>\n","<tr>\n","  <th>3</th> <td>    -154.1</td>  <td>    -141.4</td>  <td> 1.321e-67</td>  <td>    -149.0</td> \n","</tr>\n","<tr>\n","  <th>4</th> <td>    -153.7</td>  <td>    -136.8</td>  <td> 2.452e-67</td>  <td>    -146.9</td> \n","</tr>\n","<tr>\n","  <th>5</th> <td>    -153.2</td>  <td>    -132.1</td>  <td> 6.314e-67</td>  <td>    -144.6</td> \n","</tr>\n","<tr>\n","  <th>6</th> <td>    -153.2</td>  <td>    -128.0</td>  <td> 1.062e-66</td>  <td>    -143.0</td> \n","</tr>\n","<tr>\n","  <th>7</th> <td>    -153.7</td>  <td>    -124.3</td>  <td> 1.729e-66</td>  <td>    -141.8</td> \n","</tr>\n","</table>"],"text/latex":"\\begin{center}\n\\begin{tabular}{lcccc}\n\\toprule\n           & \\textbf{AIC} & \\textbf{BIC} & \\textbf{FPE} & \\textbf{HQIC}  \\\\\n\\midrule\n\\textbf{0} &      -154.4  &     -154.2*  &   8.457e-68  &      -154.3*   \\\\\n\\textbf{1} &     -155.0*  &      -150.6  &  4.711e-68*  &       -153.2   \\\\\n\\textbf{2} &      -154.8  &      -146.2  &   6.312e-68  &       -151.3   \\\\\n\\textbf{3} &      -154.1  &      -141.4  &   1.321e-67  &       -149.0   \\\\\n\\textbf{4} &      -153.7  &      -136.8  &   2.452e-67  &       -146.9   \\\\\n\\textbf{5} &      -153.2  &      -132.1  &   6.314e-67  &       -144.6   \\\\\n\\textbf{6} &      -153.2  &      -128.0  &   1.062e-66  &       -143.0   \\\\\n\\textbf{7} &      -153.7  &      -124.3  &   1.729e-66  &       -141.8   \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{VAR Order Selection (* highlights the minimums)}\n\\end{center}"},"metadata":{},"execution_count":41}],"source":["#VAR Model lag selection\n","model = VAR(data) # data[:-1]\n","ms = model.select_order(7)\n","ms.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iDIhXJAHFTc8"},"outputs":[],"source":["# Fit VAR with 6 lags and see the results\n","results  = model.fit(7)#maxlags=10, ic='aic'\n","#results.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"executionInfo":{"elapsed":352,"status":"ok","timestamp":1711360731995,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"DdyjSKfa9kOR","outputId":"ba67925e-1d39-48bf-affb-02fcc78a8f21"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.iolib.table.SimpleTable'>"],"text/html":["<table class=\"simpletable\">\n","<caption>normality (skew and kurtosis) test. H_0: data generated by normally-distributed process. Conclusion: fail to reject H_0 at 5% significance level.</caption>\n","<tr>\n","  <th>Test statistic</th> <th>Critical value</th> <th>p-value</th> <th>df</th>\n","</tr>\n","<tr>\n","       <td>20.77</td>          <td>46.19</td>      <td>0.937</td>  <td>32</td>\n","</tr>\n","</table>"],"text/latex":"\\begin{center}\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Test statistic} & \\textbf{Critical value} & \\textbf{p-value} & \\textbf{df}  \\\\\n\\midrule\n         20.77          &          46.19          &      0.937       &      32      \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{normality (skew and kurtosis) test. H_0: data generated by normally-distributed process. Conclusion: fail to reject H_0 at 5% significance level.}\n\\end{center}"},"metadata":{},"execution_count":43}],"source":["# Normality test (are residuals normally distributed?)\n","nt = results.test_normality()\n","nt.summary()\n","# yes :) they are :)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kVpZ5RlwY4dv"},"outputs":[],"source":["#results.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1711360731996,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"C3xR0EK77S-A","outputId":"23656ec7-73dc-4721-a075-017f7ae89e33"},"outputs":[{"output_type":"stream","name":"stdout","text":["[2.08830318 1.90701674 1.85604145 1.94956791 1.96168085 1.92849083\n"," 2.02816246 2.13655268 2.02964022 1.95640003 2.04296884 1.89256871\n"," 2.16783043 2.08954268 2.05131801 1.8046831 ]\n"]}],"source":["# checking autocorrelation in residuals\n","from statsmodels.stats.stattools import durbin_watson\n","out = durbin_watson(results.resid)\n","print(out)\n","# no autocorrelation, dw statistics are always nearly 2.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"executionInfo":{"elapsed":555,"status":"ok","timestamp":1711360732546,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"yYqAWs50cEMk","outputId":"68de659f-1d32-4623-8bab-a5c4838f1aa6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["2.0918724269132085e-17"]},"metadata":{},"execution_count":46},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["residuals = results.resid\n","residuals[\"GBPUSD\"].plot()\n","residuals[\"GBPUSD\"].mean()\n","#residuals[\"GBPUSD\"].var() #-8.3419732825863e-12 -1.2325951861483965e-11 9.006365659386069e-12\n","#5.700218993989929e-05 5.182073259781298e-05 4.5396751666659766e-05\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1711360732547,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"w8k-U3dRV3Xy","outputId":"8c78864e-71d5-40ed-8673-605ecad0bb87"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["9.109269968215495e-05"]},"metadata":{},"execution_count":47}],"source":["residuals[\"GBPUSD\"].var()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HjQqHe6zflat"},"outputs":[],"source":["#residuals\n","#quarterly sums of USDGBP residuals are all 0!!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":471},"executionInfo":{"elapsed":512,"status":"ok","timestamp":1711360733055,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"nDTrc2R28lPz","outputId":"c225d853-db98-4288-f0b0-dfa1b08231c5"},"outputs":[{"output_type":"stream","name":"stdout","text":["ACF\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["#results.resid\n","#results.resid_acorr(1)\n","from statsmodels.graphics.tsaplots import plot_acf\n","plot_acf(results.resid[\"GBPUSD\"], lags=24)\n","print(\"ACF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2RW8ev6yfsui"},"outputs":[],"source":["#results.plot()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T-JYBqjJf8ZV"},"outputs":[],"source":["#results.plot_acorr()"]},{"cell_type":"markdown","source":["# FEVDs"],"metadata":{"id":"o2REPVNnmGoz"}},{"cell_type":"code","source":["# You can have a look how nicely the numbers in the first column fall... It's so beautiful :) The FEVD plots here are not colorful, so I plot this in R."],"metadata":{"id":"LYYV-nBtrLXT"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":547,"status":"ok","timestamp":1711360733599,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"},"user_tz":-60},"id":"3w_MZGcr9Z1J","outputId":"c850bb0d-31e5-4e7a-a476-21f473b0fefc"},"outputs":[{"output_type":"stream","name":"stdout","text":["FEVD for GBPUSD\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     1.000000  0.000000  0.000000              0.000000              0.000000  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.753992  0.000567  0.001171              0.054820              0.000879  0.000216  0.003776  0.004638  0.161965           0.000751                   0.012459  0.001286            0.000897                  0.000021  0.000012  0.002552\n","2     0.607931  0.000749  0.003516              0.070834              0.013556  0.001039  0.003289  0.071289  0.143031           0.005511                   0.017213  0.001750            0.010717                  0.022221  0.001903  0.025449\n","3     0.521116  0.001663  0.003564              0.060132              0.017087  0.018716  0.002871  0.107439  0.120844           0.027954                   0.021315  0.001495            0.039258                  0.018966  0.013348  0.024232\n","4     0.477648  0.009670  0.003483              0.071473              0.028372  0.024858  0.004330  0.101050  0.111351           0.024899                   0.020582  0.004300            0.035190                  0.025059  0.035130  0.022605\n","5     0.431216  0.010790  0.054022              0.064719              0.036870  0.027384  0.005354  0.094044  0.100595           0.023589                   0.024039  0.010628            0.034674                  0.022651  0.034825  0.024601\n","6     0.411762  0.010928  0.058500              0.069479              0.035524  0.032763  0.006883  0.091327  0.097662           0.022736                   0.029626  0.010807            0.033217                  0.021767  0.043227  0.023792\n","7     0.392326  0.012515  0.060414              0.069864              0.034602  0.048552  0.006645  0.088139  0.093666           0.025182                   0.028379  0.012905            0.031679                  0.027171  0.042054  0.025908\n","8     0.378336  0.013125  0.058390              0.067655              0.034253  0.048669  0.008985  0.090680  0.100075           0.027685                   0.030175  0.013767            0.032018                  0.026294  0.044589  0.025305\n","9     0.365622  0.016243  0.057139              0.065297              0.034858  0.049572  0.008880  0.091569  0.096552           0.031138                   0.029111  0.014015            0.031567                  0.029001  0.048643  0.030793\n","10    0.356959  0.018707  0.056001              0.064831              0.033766  0.048341  0.009868  0.089792  0.098920           0.030388                   0.028463  0.014856            0.032871                  0.038286  0.047274  0.030675\n","11    0.348841  0.018699  0.055787              0.071145              0.033024  0.047213  0.010211  0.093731  0.096660           0.031463                   0.028041  0.015369            0.032755                  0.040946  0.046153  0.029962\n","12    0.342194  0.019210  0.057590              0.069830              0.033378  0.046779  0.011214  0.096480  0.095454           0.030865                   0.029692  0.016183            0.032383                  0.042815  0.045496  0.030437\n","13    0.334758  0.019335  0.056716              0.072353              0.034679  0.046063  0.011463  0.094620  0.096591           0.032071                   0.030048  0.015831            0.032620                  0.042393  0.048861  0.031599\n","14    0.330501  0.020007  0.058323              0.071402              0.034162  0.046149  0.013556  0.093203  0.094976           0.032563                   0.030305  0.016664            0.035517                  0.042946  0.048658  0.031069\n","15    0.328116  0.022840  0.057764              0.071047              0.034239  0.046619  0.014268  0.092424  0.093985           0.032327                   0.030375  0.016783            0.037389                  0.042768  0.048298  0.030759\n","16    0.326245  0.022743  0.058187              0.070785              0.034165  0.046347  0.014962  0.092459  0.093497           0.033174                   0.030566  0.018035            0.037616                  0.042519  0.048015  0.030684\n","17    0.324605  0.022771  0.057863              0.072343              0.034063  0.046277  0.015049  0.091931  0.093496           0.033025                   0.030383  0.018908            0.037407                  0.042279  0.048168  0.031432\n","18    0.321506  0.022586  0.059153              0.072098              0.034174  0.045817  0.014956  0.091952  0.093908           0.034077                   0.031955  0.018717            0.038171                  0.042113  0.047683  0.031134\n","19    0.320680  0.022729  0.059312              0.071919              0.034147  0.045798  0.015380  0.091666  0.094242           0.033971                   0.031856  0.019065            0.038203                  0.042134  0.047547  0.031352\n","20    0.320406  0.022697  0.059806              0.071783              0.034146  0.046150  0.015364  0.091508  0.093954           0.033954                   0.031759  0.019023            0.038106                  0.042264  0.047459  0.031622\n","21    0.319550  0.022636  0.059638              0.071788              0.034482  0.046268  0.015327  0.091256  0.093829           0.034233                   0.031731  0.018974            0.038547                  0.042797  0.047324  0.031620\n","22    0.318869  0.022610  0.059550              0.071842              0.034419  0.046662  0.015428  0.091036  0.093682           0.034253                   0.031941  0.018949            0.038510                  0.043033  0.047544  0.031670\n","23    0.317889  0.022806  0.059379              0.072027              0.034311  0.046701  0.015454  0.090897  0.094230           0.034146                   0.032381  0.018899            0.038634                  0.043099  0.047497  0.031648\n","24    0.317084  0.022739  0.059203              0.071864              0.034231  0.046584  0.015903  0.090943  0.094075           0.034118                   0.032323  0.019483            0.039068                  0.043400  0.047358  0.031623\n","25    0.316245  0.022686  0.060318              0.071695              0.034140  0.046556  0.016149  0.090796  0.093854           0.034454                   0.032282  0.019434            0.039029                  0.043319  0.047321  0.031721\n","26    0.315829  0.022849  0.060418              0.071604              0.034178  0.046603  0.016162  0.090744  0.093813           0.034417                   0.032380  0.019528            0.039213                  0.043258  0.047285  0.031719\n","27    0.315540  0.022849  0.060532              0.071588              0.034145  0.046571  0.016160  0.090814  0.093794           0.034413                   0.032442  0.019540            0.039453                  0.043228  0.047241  0.031690\n","28    0.315192  0.022823  0.060464              0.071565              0.034226  0.046744  0.016155  0.090720  0.093794           0.034430                   0.032741  0.019520            0.039456                  0.043184  0.047234  0.031753\n","29    0.314568  0.023009  0.060801              0.071782              0.034298  0.046657  0.016123  0.090683  0.093630           0.034405                   0.032821  0.019481            0.039461                  0.043101  0.047181  0.032001\n","30    0.314350  0.022991  0.060759              0.071751              0.034265  0.046670  0.016202  0.090919  0.093543           0.034398                   0.032873  0.019523            0.039454                  0.043181  0.047136  0.031985\n","31    0.314041  0.022968  0.060771              0.071717              0.034255  0.046624  0.016395  0.090836  0.093554           0.034478                   0.033035  0.019512            0.039459                  0.043145  0.047120  0.032089\n","32    0.313751  0.023038  0.060926              0.071665              0.034225  0.046707  0.016385  0.090771  0.093629           0.034463                   0.033033  0.019574            0.039460                  0.043149  0.047136  0.032090\n","33    0.313507  0.023050  0.060870              0.071673              0.034215  0.046704  0.016476  0.090695  0.093536           0.034429                   0.033445  0.019580            0.039446                  0.043211  0.047106  0.032058\n","34    0.313248  0.023052  0.060929              0.071711              0.034193  0.046692  0.016626  0.090744  0.093579           0.034401                   0.033420  0.019594            0.039413                  0.043233  0.047080  0.032087\n","35    0.313059  0.023036  0.060957              0.071689              0.034208  0.046748  0.016656  0.090712  0.093604           0.034476                   0.033410  0.019602            0.039390                  0.043270  0.047065  0.032118\n","36    0.312898  0.023045  0.060926              0.071655              0.034191  0.046763  0.016699  0.090676  0.093558           0.034463                   0.033481  0.019625            0.039529                  0.043320  0.047067  0.032103\n","37    0.312754  0.023126  0.061028              0.071678              0.034178  0.046746  0.016711  0.090631  0.093532           0.034446                   0.033497  0.019672            0.039510                  0.043300  0.047061  0.032131\n","38    0.312640  0.023187  0.061043              0.071693              0.034191  0.046780  0.016705  0.090602  0.093503           0.034437                   0.033485  0.019679            0.039547                  0.043325  0.047047  0.032136\n","39    0.312533  0.023195  0.061036              0.071687              0.034188  0.046769  0.016700  0.090570  0.093471           0.034430                   0.033658  0.019719            0.039573                  0.043315  0.047030  0.032127\n","40    0.312373  0.023203  0.061103              0.071787              0.034201  0.046789  0.016708  0.090550  0.093446           0.034413                   0.033710  0.019707            0.039558                  0.043299  0.047017  0.032137\n","41    0.312193  0.023298  0.061147              0.071773              0.034201  0.046780  0.016700  0.090514  0.093491           0.034393                   0.033727  0.019781            0.039558                  0.043303  0.046992  0.032147\n","42    0.312110  0.023295  0.061141              0.071755              0.034192  0.046769  0.016708  0.090497  0.093466           0.034384                   0.033856  0.019784            0.039608                  0.043317  0.046982  0.032138\n","43    0.311976  0.023290  0.061218              0.071798              0.034186  0.046765  0.016741  0.090475  0.093466           0.034371                   0.033908  0.019776            0.039607                  0.043299  0.046987  0.032138\n","44    0.311861  0.023317  0.061245              0.071803              0.034174  0.046771  0.016740  0.090442  0.093530           0.034359                   0.033905  0.019809            0.039610                  0.043293  0.046969  0.032173\n","45    0.311793  0.023312  0.061234              0.071788              0.034166  0.046769  0.016746  0.090431  0.093513           0.034352                   0.034003  0.019819            0.039642                  0.043304  0.046960  0.032168\n","46    0.311686  0.023326  0.061246              0.071803              0.034157  0.046777  0.016795  0.090414  0.093562           0.034341                   0.034026  0.019813            0.039641                  0.043290  0.046947  0.032178\n","47    0.311579  0.023355  0.061283              0.071804              0.034150  0.046797  0.016789  0.090383  0.093598           0.034330                   0.034024  0.019838            0.039653                  0.043295  0.046931  0.032189\n","\n","FEVD for CPI_US\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.000149  0.999851  0.000000              0.000000              0.000000  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.000182  0.931525  0.000167              0.002411              0.035229  0.002618  0.000006  0.005271  0.003134           0.000599                   0.000028  0.000467            0.012498                  0.005861  0.000003  0.000000\n","2     0.000242  0.849563  0.000189              0.036460              0.040074  0.002918  0.007339  0.017258  0.007321           0.010400                   0.002690  0.000455            0.013840                  0.006546  0.000795  0.003909\n","3     0.000346  0.815472  0.016078              0.035154              0.038706  0.004083  0.007249  0.016643  0.007092           0.014446                   0.002831  0.001236            0.013703                  0.006400  0.016622  0.003940\n","4     0.002711  0.773292  0.016965              0.044934              0.046263  0.006539  0.018603  0.015621  0.006709           0.013693                   0.003796  0.001163            0.012950                  0.012259  0.019971  0.004530\n","5     0.009374  0.720933  0.016945              0.045581              0.048755  0.008115  0.018875  0.014650  0.016879           0.029035                   0.003542  0.005587            0.020017                  0.011269  0.021301  0.009141\n","6     0.026554  0.680369  0.019006              0.043948              0.046280  0.010549  0.018204  0.022367  0.019386           0.039596                   0.005306  0.006452            0.021661                  0.010805  0.020102  0.009415\n","7     0.028343  0.633852  0.041024              0.042913              0.043536  0.010310  0.018181  0.021796  0.022193           0.066076                   0.005235  0.007384            0.020119                  0.010717  0.019575  0.008746\n","8     0.029365  0.613375  0.048576              0.042878              0.042068  0.013397  0.025935  0.021031  0.023216           0.064574                   0.005922  0.007485            0.024237                  0.010405  0.018971  0.008566\n","9     0.029958  0.597643  0.052680              0.049973              0.041193  0.015562  0.027265  0.020789  0.022966           0.062222                   0.005707  0.007206            0.023856                  0.010605  0.024079  0.008294\n","10    0.030642  0.592861  0.054144              0.049414              0.040913  0.015716  0.027044  0.021543  0.022675           0.062422                   0.005676  0.007212            0.023889                  0.010474  0.026473  0.008903\n","11    0.030899  0.577359  0.054991              0.048478              0.041932  0.026899  0.027858  0.022487  0.022646           0.061061                   0.008333  0.007530            0.024326                  0.010494  0.025926  0.008781\n","12    0.030737  0.561417  0.057740              0.047518              0.042352  0.027869  0.028211  0.022165  0.022369           0.064616                   0.011655  0.007476            0.027545                  0.010214  0.026354  0.011763\n","13    0.030869  0.556576  0.059676              0.047423              0.042094  0.028346  0.027967  0.022034  0.023077           0.065537                   0.011952  0.007517            0.027332                  0.011002  0.026592  0.012008\n","14    0.030535  0.551465  0.059592              0.048000              0.042240  0.028131  0.027788  0.022589  0.024590           0.065281                   0.013204  0.009588            0.027035                  0.010907  0.027078  0.011975\n","15    0.030418  0.546065  0.059740              0.051730              0.042135  0.028071  0.027563  0.022314  0.026334           0.064552                   0.013403  0.009691            0.026800                  0.010770  0.027951  0.012462\n","16    0.030207  0.541132  0.060069              0.051488              0.043709  0.029434  0.028475  0.022383  0.026274           0.064065                   0.014462  0.009915            0.027042                  0.010981  0.027756  0.012609\n","17    0.030020  0.537299  0.059741              0.051232              0.045117  0.029664  0.028494  0.023950  0.026436           0.065452                   0.014570  0.010079            0.026851                  0.010978  0.027599  0.012518\n","18    0.029842  0.533892  0.059807              0.050888              0.044791  0.030357  0.028399  0.023869  0.026453           0.065464                   0.014976  0.010005            0.028282                  0.012664  0.027871  0.012440\n","19    0.029645  0.531041  0.059544              0.052279              0.044636  0.030168  0.028215  0.023849  0.027247           0.065916                   0.016279  0.009953            0.028136                  0.012584  0.028085  0.012421\n","20    0.029467  0.528686  0.059419              0.051965              0.044551  0.031091  0.029462  0.023715  0.027149           0.065549                   0.016726  0.010526            0.028583                  0.012595  0.027945  0.012570\n","21    0.029295  0.525579  0.059200              0.052275              0.044538  0.033559  0.029508  0.023810  0.027159           0.065182                   0.017524  0.010680            0.028418                  0.012555  0.027822  0.012896\n","22    0.029217  0.523325  0.058996              0.052115              0.044345  0.034788  0.029357  0.024820  0.027050           0.065515                   0.017739  0.010640            0.028949                  0.012550  0.027711  0.012883\n","23    0.029297  0.522672  0.058932              0.052096              0.044422  0.034784  0.029341  0.025015  0.027122           0.065448                   0.017955  0.010708            0.028946                  0.012668  0.027702  0.012893\n","24    0.029237  0.521016  0.059644              0.051926              0.044452  0.034918  0.029276  0.024960  0.027049           0.065305                   0.017943  0.010692            0.029673                  0.013231  0.027766  0.012912\n","25    0.029129  0.519153  0.059586              0.054106              0.044287  0.034838  0.029178  0.025056  0.027101           0.065200                   0.018232  0.010684            0.029563                  0.013286  0.027732  0.012870\n","26    0.029320  0.518121  0.059467              0.054196              0.044208  0.035216  0.029421  0.025153  0.027059           0.065197                   0.018254  0.010688            0.029607                  0.013498  0.027739  0.012853\n","27    0.029426  0.517216  0.059361              0.054250              0.044361  0.035381  0.029427  0.025110  0.027053           0.065092                   0.018811  0.010683            0.029713                  0.013492  0.027788  0.012836\n","28    0.029560  0.516695  0.059414              0.054310              0.044351  0.035426  0.029405  0.025159  0.027191           0.065037                   0.018819  0.010808            0.029742                  0.013480  0.027769  0.012835\n","29    0.029534  0.516202  0.059585              0.054477              0.044305  0.035380  0.029368  0.025182  0.027154           0.064951                   0.018872  0.010796            0.029978                  0.013584  0.027785  0.012846\n","30    0.029543  0.515815  0.059587              0.054440              0.044281  0.035423  0.029361  0.025360  0.027209           0.064901                   0.018876  0.010926            0.030032                  0.013641  0.027768  0.012838\n","31    0.029512  0.515276  0.059685              0.054799              0.044231  0.035400  0.029334  0.025444  0.027194           0.064878                   0.018917  0.011078            0.030003                  0.013663  0.027749  0.012836\n","32    0.029504  0.514656  0.059618              0.054832              0.044186  0.035648  0.029317  0.025544  0.027344           0.064897                   0.018901  0.011083            0.030117                  0.013684  0.027830  0.012839\n","33    0.029477  0.514132  0.059634              0.054904              0.044159  0.035725  0.029290  0.025519  0.027370           0.064837                   0.019268  0.011123            0.030087                  0.013705  0.027943  0.012827\n","34    0.029503  0.513887  0.059828              0.054892              0.044148  0.035708  0.029304  0.025570  0.027416           0.064798                   0.019260  0.011124            0.030094                  0.013699  0.027943  0.012825\n","35    0.029491  0.513648  0.059924              0.054912              0.044155  0.035696  0.029299  0.025558  0.027407           0.064764                   0.019279  0.011120            0.030127                  0.013706  0.027928  0.012986\n","36    0.029489  0.513567  0.059920              0.054904              0.044153  0.035748  0.029295  0.025556  0.027405           0.064777                   0.019276  0.011123            0.030152                  0.013724  0.027928  0.012984\n","37    0.029511  0.513288  0.059903              0.054988              0.044129  0.035769  0.029311  0.025547  0.027520           0.064770                   0.019320  0.011125            0.030142                  0.013762  0.027919  0.012996\n","38    0.029499  0.513056  0.059874              0.054959              0.044106  0.035941  0.029333  0.025557  0.027514           0.064785                   0.019361  0.011119            0.030202                  0.013784  0.027912  0.012998\n","39    0.029493  0.512857  0.059855              0.054963              0.044126  0.035943  0.029356  0.025559  0.027541           0.064765                   0.019534  0.011115            0.030195                  0.013799  0.027903  0.012995\n","40    0.029491  0.512800  0.059897              0.054964              0.044123  0.035935  0.029371  0.025565  0.027565           0.064757                   0.019529  0.011112            0.030192                  0.013797  0.027896  0.013005\n","41    0.029487  0.512666  0.059897              0.054960              0.044117  0.035925  0.029368  0.025650  0.027558           0.064741                   0.019535  0.011126            0.030208                  0.013847  0.027897  0.013019\n","42    0.029481  0.512573  0.059886              0.054951              0.044109  0.035949  0.029372  0.025675  0.027561           0.064729                   0.019556  0.011124            0.030252                  0.013858  0.027906  0.013017\n","43    0.029477  0.512482  0.059905              0.054980              0.044101  0.035943  0.029367  0.025670  0.027573           0.064716                   0.019564  0.011126            0.030270                  0.013859  0.027938  0.013030\n","44    0.029476  0.512401  0.059897              0.054999              0.044112  0.035971  0.029376  0.025667  0.027573           0.064710                   0.019582  0.011124            0.030284                  0.013863  0.027937  0.013028\n","45    0.029474  0.512329  0.059889              0.054992              0.044108  0.035975  0.029381  0.025672  0.027570           0.064701                   0.019644  0.011133            0.030289                  0.013876  0.027938  0.013027\n","46    0.029475  0.512255  0.059901              0.055011              0.044113  0.035991  0.029389  0.025668  0.027604           0.064695                   0.019641  0.011136            0.030287                  0.013873  0.027932  0.013028\n","47    0.029473  0.512172  0.059938              0.055007              0.044113  0.035986  0.029388  0.025674  0.027603           0.064685                   0.019660  0.011155            0.030290                  0.013892  0.027935  0.013029\n","\n","FEVD for CPI_UK\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.011642  0.003335  0.985023              0.000000              0.000000  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.010420  0.031060  0.845040              0.001840              0.000104  0.036695  0.014952  0.028037  0.000292           0.003969                   0.015578  0.003763            0.001132                  0.005547  0.001387  0.000183\n","2     0.009658  0.042740  0.773932              0.002466              0.000141  0.045453  0.014108  0.040491  0.000282           0.004291                   0.017818  0.007286            0.001066                  0.012508  0.002919  0.024840\n","3     0.008609  0.037334  0.695228              0.018518              0.000353  0.070071  0.018058  0.035507  0.018580           0.005263                   0.020388  0.019973            0.012110                  0.011326  0.004448  0.024236\n","4     0.014948  0.035918  0.661978              0.019943              0.000347  0.068034  0.021985  0.039208  0.017843           0.010175                   0.019770  0.019292            0.024849                  0.015738  0.005409  0.024562\n","5     0.014503  0.035147  0.637987              0.022022              0.000366  0.076641  0.022659  0.037786  0.020076           0.011520                   0.023650  0.018718            0.032097                  0.016015  0.005595  0.025218\n","6     0.013297  0.031913  0.581168              0.019881              0.024660  0.069444  0.023039  0.038364  0.027412           0.027947                   0.022739  0.017643            0.037007                  0.016315  0.020534  0.028635\n","7     0.012712  0.039019  0.555299              0.019354              0.023672  0.068245  0.022597  0.045834  0.026317           0.026884                   0.028025  0.022919            0.041296                  0.016217  0.024256  0.027355\n","8     0.013662  0.043190  0.536332              0.018997              0.023939  0.067278  0.021985  0.045883  0.029063           0.026426                   0.036907  0.024472            0.040024                  0.019194  0.023551  0.029096\n","9     0.014039  0.042648  0.526938              0.018690              0.026339  0.066399  0.024437  0.044991  0.028553           0.026001                   0.036426  0.024296            0.043857                  0.021139  0.025212  0.030035\n","10    0.016461  0.043941  0.518075              0.018470              0.026805  0.073352  0.024051  0.044203  0.028573           0.025752                   0.035792  0.023896            0.044834                  0.021304  0.024807  0.029684\n","11    0.017182  0.044954  0.504390              0.018884              0.029249  0.073117  0.023913  0.042943  0.032385           0.025312                   0.036214  0.023848            0.051306                  0.021404  0.024950  0.029949\n","12    0.016948  0.044853  0.500906              0.019086              0.029797  0.071312  0.023385  0.041920  0.032046           0.026708                   0.036137  0.028926            0.050711                  0.021351  0.025732  0.030182\n","13    0.020283  0.048050  0.489825              0.019515              0.029251  0.070115  0.025714  0.041886  0.031975           0.026176                   0.039350  0.028682            0.052445                  0.021782  0.025254  0.029697\n","14    0.020780  0.048285  0.486560              0.019921              0.029141  0.071187  0.025563  0.041859  0.033032           0.026077                   0.039449  0.028599            0.052283                  0.021760  0.025312  0.030192\n","15    0.021183  0.048132  0.478912              0.024767              0.028812  0.070677  0.025260  0.041977  0.035420           0.025921                   0.042030  0.028186            0.051453                  0.021774  0.025513  0.029983\n","16    0.021060  0.048533  0.475745              0.025933              0.029177  0.070925  0.026218  0.042979  0.035204           0.026084                   0.041779  0.028156            0.051101                  0.021637  0.025417  0.030054\n","17    0.020934  0.048159  0.471674              0.025907              0.029708  0.072352  0.028195  0.042675  0.035273           0.026059                   0.041430  0.027976            0.052777                  0.021470  0.025525  0.029887\n","18    0.020671  0.047531  0.470595              0.026321              0.030378  0.071018  0.027711  0.043450  0.034693           0.026893                   0.042868  0.027513            0.051899                  0.021063  0.025357  0.032038\n","19    0.020613  0.047374  0.467842              0.026573              0.030182  0.070545  0.027622  0.043213  0.035487           0.026928                   0.043964  0.028543            0.052281                  0.021125  0.025423  0.032284\n","20    0.021204  0.047318  0.465402              0.026672              0.030054  0.070608  0.027607  0.043019  0.036185           0.026738                   0.046297  0.028545            0.051907                  0.020972  0.025439  0.032032\n","21    0.021229  0.048158  0.460894              0.029971              0.029794  0.070800  0.027552  0.042618  0.036824           0.026587                   0.047325  0.028754            0.051637                  0.020792  0.025278  0.031787\n","22    0.021171  0.048186  0.459557              0.029895              0.029790  0.070582  0.027586  0.042549  0.036718           0.026779                   0.047932  0.028696            0.052260                  0.021152  0.025226  0.031921\n","23    0.021364  0.048404  0.458085              0.029731              0.030183  0.070426  0.027650  0.042320  0.036543           0.026829                   0.047667  0.029339            0.053074                  0.021105  0.025408  0.031870\n","24    0.021293  0.048237  0.457572              0.029676              0.030124  0.070164  0.027733  0.042176  0.036501           0.027490                   0.048092  0.029286            0.053023                  0.021360  0.025316  0.031958\n","25    0.021150  0.048513  0.454606              0.029920              0.030006  0.070365  0.027553  0.041951  0.036575           0.027317                   0.049238  0.030007            0.054240                  0.021567  0.025249  0.031743\n","26    0.021144  0.048342  0.452791              0.029877              0.030262  0.070917  0.027440  0.042071  0.037199           0.027316                   0.050433  0.029927            0.054018                  0.021499  0.025147  0.031616\n","27    0.021079  0.048372  0.451395              0.030415              0.030184  0.070730  0.027399  0.042392  0.037836           0.027235                   0.050657  0.029856            0.053865                  0.021492  0.025268  0.031824\n","28    0.021214  0.048371  0.450882              0.030372              0.030167  0.070656  0.027376  0.042696  0.037823           0.027212                   0.050599  0.029822            0.054016                  0.021523  0.025341  0.031929\n","29    0.021224  0.048271  0.449921              0.030296              0.030291  0.070874  0.027454  0.042565  0.037842           0.027471                   0.050676  0.029859            0.054435                  0.021502  0.025320  0.031997\n","30    0.021137  0.048376  0.449564              0.030374              0.030229  0.070619  0.027417  0.042387  0.038340           0.027739                   0.050721  0.029873            0.054492                  0.021436  0.025229  0.032070\n","31    0.021111  0.048639  0.448567              0.030301              0.030180  0.070610  0.027384  0.042284  0.038244           0.027720                   0.051403  0.029840            0.054975                  0.021466  0.025267  0.032009\n","32    0.021067  0.048558  0.447562              0.030230              0.030165  0.070819  0.027477  0.042346  0.038698           0.027653                   0.051995  0.029842            0.054866                  0.021519  0.025236  0.031965\n","33    0.021068  0.048765  0.446522              0.030595              0.030128  0.070892  0.027647  0.042272  0.039255           0.027705                   0.052031  0.029774            0.054713                  0.021469  0.025174  0.031990\n","34    0.021044  0.048698  0.446047              0.030579              0.030088  0.070791  0.027669  0.042510  0.039228           0.027665                   0.052166  0.029732            0.054922                  0.021628  0.025185  0.032049\n","35    0.021017  0.048668  0.445500              0.030535              0.030071  0.070828  0.027858  0.042469  0.039224           0.027672                   0.052393  0.029791            0.055130                  0.021647  0.025150  0.032047\n","36    0.021000  0.048731  0.445249              0.030582              0.030024  0.070744  0.027831  0.042458  0.039390           0.027656                   0.052386  0.029766            0.055194                  0.021620  0.025173  0.032198\n","37    0.020966  0.048781  0.444525              0.030539              0.029979  0.070676  0.027798  0.042384  0.039401           0.027708                   0.052852  0.029892            0.055460                  0.021726  0.025163  0.032149\n","38    0.020939  0.048754  0.443921              0.030563              0.029946  0.070731  0.027814  0.042377  0.039688           0.027702                   0.053185  0.029869            0.055409                  0.021830  0.025158  0.032115\n","39    0.020910  0.048985  0.443344              0.030812              0.029940  0.070816  0.027789  0.042312  0.040004           0.027663                   0.053180  0.029891            0.055340                  0.021797  0.025125  0.032094\n","40    0.020885  0.048940  0.442911              0.030838              0.029929  0.070758  0.027860  0.042337  0.039955           0.027635                   0.053305  0.029894            0.055553                  0.021978  0.025128  0.032094\n","41    0.020882  0.048888  0.442598              0.030821              0.029958  0.070744  0.027935  0.042284  0.039942           0.027634                   0.053481  0.029864            0.055734                  0.022017  0.025110  0.032108\n","42    0.020862  0.048880  0.442436              0.030891              0.029976  0.070676  0.027931  0.042233  0.040096           0.027624                   0.053515  0.029843            0.055724                  0.022001  0.025112  0.032199\n","43    0.020836  0.048905  0.441943              0.030875              0.029943  0.070597  0.027899  0.042180  0.040127           0.027601                   0.053918  0.029896            0.055967                  0.022057  0.025080  0.032175\n","44    0.020816  0.048882  0.441500              0.030951              0.029911  0.070631  0.027924  0.042253  0.040249           0.027573                   0.054175  0.029865            0.055941                  0.022127  0.025055  0.032146\n","45    0.020791  0.049012  0.441073              0.031211              0.029913  0.070679  0.027912  0.042226  0.040495           0.027544                   0.054128  0.029860            0.055877                  0.022100  0.025027  0.032152\n","46    0.020788  0.048982  0.440833              0.031201              0.029891  0.070630  0.027923  0.042242  0.040486           0.027523                   0.054193  0.029854            0.056067                  0.022190  0.025021  0.032174\n","47    0.020787  0.048951  0.440505              0.031237              0.029919  0.070637  0.027977  0.042202  0.040506           0.027522                   0.054349  0.029833            0.056224                  0.022193  0.024999  0.032158\n","\n","FEVD for Money Market Rate_US\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.034823  0.011428  0.015148              0.938600              0.000000  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.032537  0.016810  0.012385              0.835238              0.009630  0.006044  0.001767  0.024206  0.007680           0.016363                   0.014120  0.015298            0.000451                  0.003619  0.001274  0.002577\n","2     0.036648  0.020902  0.010772              0.720919              0.008188  0.008105  0.018183  0.052345  0.008776           0.033385                   0.014116  0.018169            0.006731                  0.031460  0.003331  0.007970\n","3     0.068645  0.018318  0.010617              0.649510              0.008921  0.035623  0.015934  0.055750  0.010649           0.029527                   0.012373  0.015818            0.012894                  0.027394  0.002943  0.025083\n","4     0.065451  0.016970  0.012702              0.615943              0.010850  0.056172  0.016911  0.050697  0.009882           0.029338                   0.011634  0.014373            0.012693                  0.029632  0.007130  0.039624\n","5     0.063029  0.021838  0.016073              0.580373              0.010683  0.072846  0.022203  0.049675  0.012325           0.027823                   0.014358  0.015103            0.013615                  0.032488  0.006736  0.040833\n","6     0.069840  0.022581  0.015657              0.553074              0.011362  0.082975  0.021483  0.049058  0.013718           0.027005                   0.014283  0.019182            0.013054                  0.031004  0.012507  0.043217\n","7     0.066808  0.024180  0.016759              0.534367              0.014743  0.089189  0.021459  0.054480  0.018440           0.025343                   0.013755  0.018002            0.012273                  0.034270  0.015375  0.040557\n","8     0.064330  0.028846  0.016748              0.517672              0.017355  0.087134  0.022625  0.059229  0.019883           0.024339                   0.013472  0.018967            0.012705                  0.035796  0.021265  0.039635\n","9     0.062048  0.033622  0.027209              0.497640              0.018047  0.086425  0.021808  0.057962  0.021933           0.023721                   0.013384  0.018313            0.013725                  0.035057  0.030767  0.038340\n","10    0.060429  0.033325  0.033377              0.490429              0.018003  0.084013  0.022357  0.057323  0.021437           0.023266                   0.013034  0.018041            0.014824                  0.034334  0.036825  0.038984\n","11    0.059381  0.034972  0.034760              0.482638              0.017614  0.084108  0.022347  0.056041  0.020961           0.022992                   0.012795  0.017722            0.015448                  0.037301  0.039870  0.041050\n","12    0.059252  0.035003  0.035325              0.480205              0.017572  0.083648  0.022263  0.056916  0.020878           0.023671                   0.013399  0.017684            0.015785                  0.037310  0.040280  0.040810\n","13    0.059879  0.034490  0.038270              0.473306              0.017455  0.082440  0.022437  0.056187  0.023290           0.023542                   0.016593  0.017454            0.015891                  0.037849  0.039954  0.040962\n","14    0.060464  0.034194  0.039162              0.468928              0.017706  0.083849  0.023440  0.055707  0.023613           0.023387                   0.017545  0.017297            0.016924                  0.037504  0.039693  0.040587\n","15    0.060776  0.034269  0.039712              0.465127              0.018841  0.083547  0.023414  0.055227  0.023413           0.023946                   0.017723  0.017473            0.017349                  0.037585  0.039482  0.042117\n","16    0.060489  0.034771  0.039354              0.461649              0.019516  0.083001  0.024015  0.057478  0.023260           0.023830                   0.017828  0.018015            0.017422                  0.037302  0.039268  0.042803\n","17    0.060166  0.037365  0.039646              0.459114              0.019892  0.082677  0.023959  0.057792  0.023135           0.023699                   0.017858  0.017985            0.017332                  0.037198  0.039101  0.043082\n","18    0.059908  0.038065  0.039641              0.457045              0.019822  0.082484  0.023857  0.057704  0.023168           0.024530                   0.018976  0.017970            0.017294                  0.037030  0.039609  0.042897\n","19    0.060355  0.039436  0.039567              0.455034              0.019969  0.082224  0.023744  0.057529  0.023086           0.025195                   0.018915  0.017904            0.017906                  0.036876  0.039567  0.042692\n","20    0.060152  0.039404  0.040293              0.453550              0.019902  0.081944  0.023668  0.057334  0.023388           0.025183                   0.019720  0.017938            0.018080                  0.036815  0.039430  0.043198\n","21    0.060005  0.039303  0.041135              0.452395              0.019884  0.081822  0.023641  0.057196  0.023336           0.025772                   0.019717  0.017893            0.018146                  0.036946  0.039624  0.043185\n","22    0.059875  0.039317  0.041077              0.451755              0.019841  0.081683  0.023817  0.057360  0.023294           0.026297                   0.019741  0.018069            0.018205                  0.036866  0.039618  0.043186\n","23    0.059980  0.039259  0.041603              0.450940              0.019802  0.081697  0.023830  0.057256  0.023546           0.026312                   0.019716  0.018031            0.018175                  0.036805  0.039954  0.043093\n","24    0.059843  0.039359  0.041978              0.449829              0.020558  0.081908  0.023780  0.057192  0.023489           0.026253                   0.019681  0.017994            0.018250                  0.036716  0.040168  0.043002\n","25    0.059877  0.039304  0.041882              0.448904              0.020553  0.082214  0.023810  0.057346  0.023597           0.026523                   0.019696  0.018412            0.018209                  0.036663  0.040075  0.042935\n","26    0.059949  0.039301  0.041849              0.448664              0.020605  0.082288  0.023829  0.057326  0.023714           0.026509                   0.019686  0.018437            0.018195                  0.036638  0.040054  0.042956\n","27    0.059951  0.039335  0.041854              0.448582              0.020635  0.082287  0.023834  0.057318  0.023716           0.026504                   0.019682  0.018436            0.018207                  0.036631  0.040078  0.042949\n","28    0.059911  0.039338  0.041842              0.448291              0.020682  0.082412  0.023904  0.057302  0.023808           0.026502                   0.019672  0.018425            0.018238                  0.036606  0.040083  0.042984\n","29    0.059856  0.039310  0.041804              0.448257              0.020783  0.082464  0.023930  0.057276  0.023793           0.026558                   0.019654  0.018411            0.018224                  0.036587  0.040048  0.043046\n","30    0.059775  0.039267  0.041989              0.447649              0.020763  0.082686  0.023901  0.057361  0.023903           0.026522                   0.019636  0.018645            0.018205                  0.036545  0.040100  0.043052\n","31    0.059748  0.039240  0.042045              0.447351              0.020770  0.082789  0.023909  0.057377  0.023903           0.026613                   0.019680  0.018635            0.018317                  0.036520  0.040081  0.043021\n","32    0.059807  0.039234  0.042073              0.447200              0.020760  0.082746  0.023925  0.057400  0.023896           0.026615                   0.019723  0.018632            0.018307                  0.036514  0.040167  0.042999\n","33    0.059780  0.039218  0.042094              0.447051              0.020761  0.082748  0.023924  0.057417  0.023894           0.026668                   0.019726  0.018677            0.018299                  0.036528  0.040236  0.042980\n","34    0.059754  0.039203  0.042085              0.446857              0.020827  0.082829  0.023923  0.057405  0.023907           0.026672                   0.019724  0.018669            0.018444                  0.036513  0.040222  0.042965\n","35    0.059738  0.039195  0.042082              0.446785              0.020825  0.082806  0.023945  0.057395  0.023955           0.026716                   0.019737  0.018682            0.018457                  0.036504  0.040224  0.042954\n","36    0.059741  0.039199  0.042083              0.446701              0.020821  0.082839  0.023949  0.057400  0.023954           0.026718                   0.019749  0.018715            0.018465                  0.036499  0.040219  0.042946\n","37    0.059757  0.039223  0.042081              0.446619              0.020819  0.082913  0.023949  0.057389  0.023951           0.026714                   0.019746  0.018729            0.018462                  0.036492  0.040211  0.042945\n","38    0.059738  0.039219  0.042080              0.446549              0.020842  0.082886  0.023958  0.057440  0.023962           0.026770                   0.019745  0.018728            0.018458                  0.036483  0.040204  0.042936\n","39    0.059731  0.039219  0.042084              0.446466              0.020847  0.082869  0.023953  0.057460  0.023992           0.026767                   0.019744  0.018788            0.018478                  0.036476  0.040196  0.042932\n","40    0.059714  0.039209  0.042077              0.446351              0.020862  0.082901  0.023951  0.057477  0.024005           0.026765                   0.019777  0.018831            0.018504                  0.036466  0.040190  0.042921\n","41    0.059698  0.039200  0.042108              0.446287              0.020859  0.082884  0.023955  0.057512  0.024039           0.026796                   0.019773  0.018828            0.018504                  0.036462  0.040184  0.042912\n","42    0.059698  0.039197  0.042108              0.446240              0.020857  0.082895  0.023952  0.057510  0.024044           0.026798                   0.019786  0.018827            0.018510                  0.036460  0.040197  0.042921\n","43    0.059697  0.039199  0.042106              0.446191              0.020858  0.082913  0.023955  0.057515  0.024052           0.026800                   0.019796  0.018829            0.018520                  0.036457  0.040196  0.042917\n","44    0.059691  0.039200  0.042103              0.446161              0.020860  0.082905  0.023958  0.057514  0.024088           0.026800                   0.019794  0.018842            0.018519                  0.036454  0.040192  0.042920\n","45    0.059685  0.039197  0.042117              0.446127              0.020863  0.082899  0.023962  0.057510  0.024086           0.026799                   0.019795  0.018841            0.018550                  0.036465  0.040188  0.042917\n","46    0.059681  0.039194  0.042114              0.446097              0.020861  0.082911  0.023960  0.057508  0.024092           0.026813                   0.019806  0.018845            0.018554                  0.036462  0.040186  0.042915\n","47    0.059678  0.039196  0.042130              0.446078              0.020860  0.082905  0.023959  0.057511  0.024096           0.026819                   0.019805  0.018844            0.018559                  0.036461  0.040183  0.042918\n","\n","FEVD for Money Market Rate_UK\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.000010  0.019810  0.001153              0.013877              0.965150  0.000000  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.000014  0.020430  0.014060              0.012872              0.871734  0.000090  0.000130  0.004367  0.024507           0.015889                   0.013830  0.002026            0.007094                  0.000203  0.004749  0.008006\n","2     0.004972  0.021299  0.016415              0.014001              0.818375  0.001931  0.000753  0.003757  0.021640           0.013876                   0.021243  0.005098            0.017543                  0.012135  0.004989  0.021973\n","3     0.012776  0.037397  0.015999              0.013380              0.767499  0.015582  0.003391  0.004114  0.023020           0.022659                   0.022763  0.005863            0.016479                  0.011336  0.005196  0.022545\n","4     0.011811  0.034688  0.019777              0.013253              0.735244  0.014587  0.015190  0.013623  0.029753           0.023785                   0.021240  0.005403            0.015274                  0.010682  0.015010  0.020680\n","5     0.011113  0.037539  0.034093              0.014469              0.694362  0.013722  0.016980  0.012805  0.033719           0.024842                   0.026523  0.005361            0.014405                  0.010435  0.019521  0.030113\n","6     0.011612  0.036393  0.033864              0.017694              0.675778  0.013426  0.017016  0.013912  0.036252           0.024776                   0.030858  0.007478            0.014088                  0.015961  0.021461  0.029429\n","7     0.025412  0.035039  0.031826              0.017290              0.644786  0.012769  0.018031  0.014924  0.034890           0.032235                   0.036175  0.007651            0.016775                  0.015557  0.020562  0.036077\n","8     0.028373  0.034064  0.033388              0.018645              0.630419  0.013041  0.019258  0.014836  0.038085           0.033486                   0.041117  0.007679            0.016294                  0.015560  0.020801  0.034954\n","9     0.029684  0.034163  0.032845              0.021714              0.619323  0.013942  0.018916  0.014569  0.041439           0.034197                   0.040663  0.007878            0.016833                  0.016937  0.020520  0.036376\n","10    0.029544  0.033982  0.033646              0.021550              0.614842  0.015036  0.019407  0.014590  0.042134           0.034307                   0.040758  0.007930            0.018256                  0.016795  0.020816  0.036406\n","11    0.029189  0.033474  0.033384              0.022246              0.606830  0.017540  0.019578  0.014433  0.041640           0.036586                   0.040199  0.007812            0.018170                  0.016920  0.020599  0.041401\n","12    0.031747  0.033258  0.033301              0.022116              0.602101  0.017424  0.019578  0.015378  0.041867           0.036580                   0.039884  0.007928            0.019506                  0.017157  0.020581  0.041594\n","13    0.031877  0.032851  0.034317              0.025833              0.593892  0.017180  0.019556  0.015247  0.041419           0.037023                   0.042787  0.009256            0.020130                  0.017181  0.020372  0.041078\n","14    0.031389  0.033142  0.036466              0.026968              0.584094  0.019085  0.019233  0.015588  0.040764           0.038458                   0.044022  0.009476            0.020441                  0.017475  0.021044  0.042356\n","15    0.031212  0.032964  0.038081              0.027985              0.580842  0.020107  0.019218  0.015544  0.041035           0.038428                   0.043967  0.009480            0.020526                  0.017491  0.020968  0.042153\n","16    0.031707  0.032698  0.038339              0.028207              0.576413  0.021867  0.019065  0.015834  0.041964           0.038241                   0.043823  0.010090            0.021040                  0.017772  0.020907  0.042035\n","17    0.031874  0.033202  0.038167              0.028534              0.572851  0.023248  0.019359  0.017084  0.041714           0.038428                   0.043714  0.010029            0.021522                  0.017663  0.020810  0.041799\n","18    0.032566  0.032833  0.038410              0.031389              0.567280  0.023398  0.019178  0.018003  0.041274           0.038847                   0.043999  0.009932            0.021697                  0.018994  0.020861  0.041339\n","19    0.032694  0.032985  0.039430              0.032663              0.563169  0.023236  0.019064  0.019396  0.041248           0.038858                   0.043813  0.009980            0.021652                  0.019010  0.021639  0.041162\n","20    0.032704  0.032883  0.039515              0.032846              0.561245  0.023433  0.019022  0.019329  0.041153           0.038930                   0.043659  0.009984            0.021614                  0.020484  0.021730  0.041468\n","21    0.032867  0.032873  0.039442              0.033137              0.560197  0.023625  0.018987  0.019362  0.041078           0.038870                   0.043590  0.010404            0.021592                  0.020507  0.022079  0.041390\n","22    0.032822  0.032812  0.039618              0.033242              0.559439  0.023679  0.018969  0.019379  0.041004           0.038839                   0.043538  0.010854            0.021625                  0.020499  0.022307  0.041373\n","23    0.032800  0.032810  0.039838              0.033216              0.559062  0.023751  0.018971  0.019402  0.040972           0.038822                   0.043510  0.010856            0.021771                  0.020537  0.022328  0.041354\n","24    0.032752  0.032777  0.039780              0.033617              0.558864  0.023732  0.018961  0.019376  0.040951           0.038833                   0.043448  0.010858            0.021885                  0.020551  0.022297  0.041318\n","25    0.032644  0.032674  0.039792              0.033685              0.557361  0.023666  0.019250  0.019715  0.040817           0.038699                   0.043408  0.011625            0.022144                  0.020567  0.022532  0.041418\n","26    0.032638  0.032638  0.039744              0.033708              0.556701  0.023669  0.019250  0.019834  0.040948           0.038684                   0.043400  0.011943            0.022118                  0.020653  0.022647  0.041423\n","27    0.032623  0.032641  0.039721              0.033791              0.556400  0.023732  0.019315  0.019823  0.040928           0.038663                   0.043505  0.011949            0.022106                  0.020649  0.022752  0.041401\n","28    0.032682  0.032840  0.039695              0.033792              0.556086  0.023726  0.019303  0.019815  0.040900           0.038681                   0.043499  0.011943            0.022096                  0.020655  0.022910  0.041375\n","29    0.032695  0.032831  0.039673              0.033971              0.555739  0.023839  0.019300  0.019814  0.040879           0.038717                   0.043539  0.011943            0.022083                  0.020665  0.022950  0.041362\n","30    0.032852  0.032821  0.039659              0.033955              0.555493  0.023840  0.019318  0.019816  0.040976           0.038707                   0.043522  0.011950            0.022111                  0.020659  0.022942  0.041379\n","31    0.032845  0.032907  0.039639              0.033936              0.555210  0.023840  0.019474  0.019819  0.041033           0.038694                   0.043504  0.011943            0.022169                  0.020647  0.022939  0.041398\n","32    0.032868  0.032888  0.039732              0.033978              0.554858  0.023914  0.019483  0.019812  0.041008           0.038781                   0.043477  0.012110            0.022155                  0.020637  0.022927  0.041373\n","33    0.032858  0.033007  0.039762              0.033977              0.554651  0.023905  0.019477  0.019804  0.041023           0.038766                   0.043500  0.012164            0.022178                  0.020634  0.022937  0.041357\n","34    0.032875  0.033018  0.039764              0.033985              0.554446  0.023980  0.019480  0.019839  0.041065           0.038777                   0.043531  0.012160            0.022170                  0.020636  0.022929  0.041346\n","35    0.032864  0.033011  0.039845              0.034066              0.554196  0.023973  0.019480  0.019832  0.041076           0.038858                   0.043526  0.012180            0.022166                  0.020652  0.022933  0.041340\n","36    0.032892  0.033007  0.039843              0.034068              0.554081  0.023970  0.019479  0.019836  0.041078           0.038861                   0.043523  0.012180            0.022229                  0.020672  0.022929  0.041352\n","37    0.032885  0.033048  0.039835              0.034065              0.553984  0.023991  0.019490  0.019856  0.041109           0.038856                   0.043521  0.012181            0.022229                  0.020669  0.022924  0.041356\n","38    0.032883  0.033041  0.039842              0.034149              0.553866  0.023989  0.019486  0.019857  0.041123           0.038880                   0.043523  0.012194            0.022226                  0.020667  0.022921  0.041354\n","39    0.032877  0.033049  0.039837              0.034143              0.553775  0.024012  0.019487  0.019854  0.041122           0.038888                   0.043559  0.012199            0.022258                  0.020668  0.022926  0.041346\n","40    0.032870  0.033067  0.039827              0.034139              0.553631  0.024132  0.019488  0.019855  0.041135           0.038880                   0.043585  0.012206            0.022255                  0.020663  0.022932  0.041336\n","41    0.032872  0.033069  0.039901              0.034155              0.553476  0.024129  0.019496  0.019850  0.041151           0.038886                   0.043604  0.012212            0.022254                  0.020657  0.022931  0.041356\n","42    0.032872  0.033065  0.039905              0.034163              0.553413  0.024134  0.019500  0.019854  0.041147           0.038884                   0.043606  0.012211            0.022285                  0.020675  0.022934  0.041352\n","43    0.032871  0.033062  0.039903              0.034161              0.553353  0.024169  0.019498  0.019852  0.041146           0.038882                   0.043631  0.012213            0.022301                  0.020674  0.022931  0.041351\n","44    0.032867  0.033067  0.039908              0.034165              0.553281  0.024168  0.019501  0.019856  0.041177           0.038877                   0.043625  0.012223            0.022325                  0.020671  0.022931  0.041357\n","45    0.032865  0.033064  0.039907              0.034163              0.553240  0.024184  0.019503  0.019855  0.041174           0.038883                   0.043650  0.012222            0.022330                  0.020673  0.022931  0.041355\n","46    0.032861  0.033066  0.039904              0.034161              0.553180  0.024228  0.019501  0.019856  0.041183           0.038886                   0.043659  0.012224            0.022331                  0.020674  0.022935  0.041351\n","47    0.032857  0.033079  0.039951              0.034169              0.553098  0.024225  0.019501  0.019855  0.041192           0.038884                   0.043656  0.012226            0.022338                  0.020671  0.022934  0.041363\n","\n","FEVD for IPI_US\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.000054  0.018261  0.000771              0.000792              0.022564  0.957558  0.000000  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.006811  0.019757  0.001620              0.002376              0.021688  0.895622  0.000196  0.003915  0.004601           0.007290                   0.020388  0.007488            0.003988                  0.000026  0.003954  0.000281\n","2     0.058466  0.017199  0.003893              0.001954              0.019814  0.755285  0.003121  0.005994  0.023022           0.033675                   0.033855  0.015516            0.016187                  0.000310  0.003218  0.008490\n","3     0.058671  0.017195  0.007634              0.001960              0.028751  0.715174  0.002979  0.008690  0.023404           0.031480                   0.031631  0.015039            0.015891                  0.002429  0.030418  0.008656\n","4     0.065680  0.016984  0.007435              0.003418              0.034801  0.665949  0.006808  0.008152  0.023297           0.055241                   0.029442  0.014287            0.017890                  0.002280  0.028754  0.019582\n","5     0.072016  0.016794  0.007254              0.003692              0.037094  0.647487  0.006736  0.007794  0.022602           0.064787                   0.028464  0.015556            0.018292                  0.005022  0.027437  0.018972\n","6     0.071790  0.019605  0.007599              0.004610              0.037791  0.634086  0.006592  0.008059  0.023695           0.064022                   0.027629  0.014993            0.017895                  0.005016  0.038290  0.018327\n","7     0.070859  0.020054  0.007716              0.005957              0.047998  0.619613  0.006956  0.008708  0.023446           0.062968                   0.031702  0.015179            0.017869                  0.005061  0.038003  0.017910\n","8     0.081853  0.022449  0.009572              0.005970              0.047360  0.604053  0.006782  0.008859  0.022984           0.061479                   0.032056  0.015639            0.019246                  0.005033  0.037479  0.019185\n","9     0.079812  0.022210  0.010073              0.006201              0.055165  0.590575  0.006613  0.011967  0.022485           0.062453                   0.033702  0.015577            0.020345                  0.006021  0.038043  0.018758\n","10    0.078770  0.022576  0.010015              0.006607              0.054860  0.582065  0.006887  0.011802  0.023652           0.065275                   0.034764  0.017726            0.021416                  0.005942  0.038984  0.018660\n","11    0.078787  0.022887  0.010057              0.006792              0.054589  0.575621  0.008159  0.013015  0.023397           0.066560                   0.034666  0.018401            0.021634                  0.008379  0.038566  0.018491\n","12    0.078582  0.023973  0.010132              0.010396              0.054258  0.568716  0.008252  0.012865  0.024017           0.066512                   0.035506  0.019281            0.021430                  0.009613  0.038127  0.018340\n","13    0.078724  0.024226  0.010107              0.010373              0.054189  0.566843  0.008229  0.012819  0.024223           0.066279                   0.036903  0.019453            0.021456                  0.009635  0.038262  0.018278\n","14    0.077937  0.024491  0.010122              0.010282              0.053593  0.562016  0.008131  0.012839  0.024251           0.066804                   0.036803  0.019335            0.021254                  0.009703  0.042343  0.020098\n","15    0.077858  0.024369  0.010101              0.010454              0.053878  0.559744  0.008160  0.013014  0.024244           0.066759                   0.037376  0.019277            0.021148                  0.011047  0.042419  0.020153\n","16    0.077870  0.024466  0.010269              0.012122              0.053611  0.556920  0.008398  0.012947  0.024336           0.066691                   0.037213  0.019780            0.021581                  0.011508  0.042231  0.020058\n","17    0.077638  0.024460  0.010232              0.012078              0.053440  0.555278  0.008493  0.012951  0.024363           0.067787                   0.037136  0.020201            0.021781                  0.012066  0.042084  0.020013\n","18    0.077436  0.024564  0.010213              0.012066              0.053308  0.553853  0.008708  0.012986  0.024380           0.067897                   0.037053  0.020264            0.021758                  0.013134  0.042144  0.020237\n","19    0.077869  0.024504  0.010811              0.012415              0.053241  0.552248  0.008682  0.013368  0.024442           0.067883                   0.037010  0.020206            0.021714                  0.013324  0.042034  0.020247\n","20    0.077966  0.024572  0.010960              0.012410              0.053204  0.551436  0.008667  0.013466  0.024491           0.067964                   0.036954  0.020199            0.021855                  0.013396  0.042082  0.020378\n","21    0.077907  0.024689  0.010990              0.012401              0.053162  0.551041  0.008661  0.013661  0.024552           0.067986                   0.037006  0.020194            0.021840                  0.013392  0.042050  0.020469\n","22    0.077862  0.024710  0.010982              0.012811              0.053103  0.550457  0.008662  0.013646  0.024638           0.067909                   0.037034  0.020177            0.021821                  0.013379  0.042266  0.020542\n","23    0.077807  0.024743  0.011072              0.012814              0.053053  0.550015  0.008713  0.013656  0.024784           0.068006                   0.037002  0.020250            0.021845                  0.013456  0.042227  0.020558\n","24    0.077745  0.024886  0.011200              0.012834              0.053031  0.549659  0.008716  0.013800  0.024771           0.067963                   0.037013  0.020253            0.021901                  0.013491  0.042192  0.020545\n","25    0.077769  0.024880  0.011213              0.013119              0.053034  0.549342  0.008718  0.013792  0.024768           0.067959                   0.037004  0.020245            0.021932                  0.013483  0.042207  0.020533\n","26    0.077725  0.024881  0.011207              0.013126              0.053011  0.549135  0.008738  0.013892  0.024793           0.067948                   0.036982  0.020265            0.021934                  0.013634  0.042208  0.020522\n","27    0.077686  0.024979  0.011313              0.013165              0.052983  0.548856  0.008772  0.013886  0.024810           0.067918                   0.037001  0.020353            0.021946                  0.013630  0.042190  0.020513\n","28    0.077643  0.024966  0.011326              0.013158              0.052956  0.548595  0.009031  0.013882  0.024806           0.067897                   0.036990  0.020345            0.021935                  0.013624  0.042279  0.020567\n","29    0.077616  0.024971  0.011355              0.013154              0.052937  0.548543  0.009071  0.013879  0.024827           0.067872                   0.036979  0.020389            0.021931                  0.013621  0.042263  0.020593\n","30    0.077598  0.024979  0.011353              0.013155              0.052976  0.548415  0.009070  0.013899  0.024875           0.067858                   0.036998  0.020424            0.021929                  0.013628  0.042254  0.020589\n","31    0.077568  0.025028  0.011363              0.013171              0.052982  0.548180  0.009171  0.013932  0.024869           0.067848                   0.037023  0.020417            0.021926                  0.013624  0.042262  0.020634\n","32    0.077560  0.025025  0.011368              0.013172              0.052981  0.548125  0.009172  0.013963  0.024873           0.067861                   0.037019  0.020444            0.021923                  0.013625  0.042259  0.020631\n","33    0.077554  0.025019  0.011367              0.013183              0.052972  0.548008  0.009172  0.014010  0.024868           0.067893                   0.037054  0.020443            0.021926                  0.013634  0.042265  0.020631\n","34    0.077550  0.025109  0.011391              0.013181              0.052966  0.547909  0.009199  0.014009  0.024864           0.067881                   0.037047  0.020447            0.021922                  0.013631  0.042266  0.020630\n","35    0.077533  0.025122  0.011418              0.013212              0.052963  0.547778  0.009198  0.014012  0.024865           0.067870                   0.037046  0.020493            0.021923                  0.013631  0.042265  0.020672\n","36    0.077525  0.025136  0.011417              0.013210              0.052973  0.547713  0.009216  0.014010  0.024863           0.067863                   0.037054  0.020530            0.021924                  0.013636  0.042260  0.020670\n","37    0.077515  0.025131  0.011420              0.013220              0.052973  0.547608  0.009257  0.014012  0.024875           0.067853                   0.037075  0.020529            0.021941                  0.013633  0.042268  0.020691\n","38    0.077506  0.025129  0.011435              0.013235              0.052966  0.547542  0.009258  0.014017  0.024891           0.067845                   0.037071  0.020577            0.021939                  0.013633  0.042263  0.020694\n","39    0.077502  0.025128  0.011437              0.013235              0.052966  0.547513  0.009259  0.014018  0.024894           0.067843                   0.037101  0.020577            0.021940                  0.013632  0.042261  0.020694\n","40    0.077496  0.025135  0.011436              0.013235              0.052965  0.547481  0.009271  0.014026  0.024893           0.067839                   0.037106  0.020575            0.021956                  0.013632  0.042259  0.020696\n","41    0.077489  0.025132  0.011449              0.013257              0.052960  0.547432  0.009273  0.014025  0.024900           0.067835                   0.037105  0.020591            0.021954                  0.013631  0.042256  0.020710\n","42    0.077493  0.025132  0.011451              0.013257              0.052958  0.547413  0.009279  0.014024  0.024900           0.067832                   0.037105  0.020596            0.021961                  0.013632  0.042257  0.020710\n","43    0.077488  0.025138  0.011452              0.013261              0.052963  0.547387  0.009283  0.014026  0.024900           0.067830                   0.037111  0.020594            0.021970                  0.013631  0.042255  0.020712\n","44    0.077483  0.025141  0.011457              0.013271              0.052960  0.547364  0.009283  0.014025  0.024910           0.067828                   0.037108  0.020604            0.021970                  0.013630  0.042253  0.020712\n","45    0.077478  0.025143  0.011457              0.013270              0.052957  0.547331  0.009284  0.014028  0.024916           0.067824                   0.037136  0.020607            0.021974                  0.013630  0.042253  0.020712\n","46    0.077473  0.025145  0.011461              0.013270              0.052954  0.547304  0.009290  0.014033  0.024918           0.067819                   0.037140  0.020612            0.021986                  0.013629  0.042251  0.020714\n","47    0.077468  0.025145  0.011481              0.013274              0.052951  0.547268  0.009299  0.014032  0.024926           0.067817                   0.037139  0.020619            0.021984                  0.013628  0.042250  0.020718\n","\n","FEVD for IPI_UK\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.001985  0.004979  0.002911              0.024200              0.000263  0.001969  0.963694  0.000000  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.008361  0.016947  0.011947              0.017639              0.006148  0.081699  0.776965  0.003144  0.001619           0.000265                   0.007242  0.047518            0.002485                  0.005224  0.003975  0.008824\n","2     0.010205  0.029768  0.011142              0.016650              0.023806  0.079728  0.728856  0.003248  0.004654           0.003623                   0.007259  0.051028            0.002593                  0.005316  0.011712  0.010412\n","3     0.025649  0.041190  0.010310              0.031963              0.022439  0.071075  0.646622  0.009581  0.007799           0.026353                   0.007658  0.053236            0.002911                  0.019625  0.010705  0.012886\n","4     0.024894  0.042899  0.015487              0.029941              0.021296  0.066613  0.606383  0.010740  0.008201           0.025026                   0.010126  0.078791            0.003217                  0.018691  0.012939  0.024759\n","5     0.031438  0.040525  0.015168              0.029763              0.021298  0.078510  0.567714  0.010832  0.009477           0.050587                   0.010245  0.073567            0.003579                  0.017423  0.012585  0.027288\n","6     0.030665  0.066640  0.013961              0.027065              0.020050  0.071391  0.514223  0.009949  0.013717           0.075878                   0.009316  0.069134            0.017314                  0.019199  0.011409  0.030091\n","7     0.031524  0.068344  0.013916              0.027278              0.019690  0.070615  0.501019  0.010840  0.021374           0.073772                   0.009524  0.067914            0.018990                  0.019831  0.011185  0.034182\n","8     0.032753  0.066700  0.013892              0.027744              0.018924  0.068036  0.481489  0.011941  0.020488           0.077643                   0.011879  0.081810            0.020578                  0.019249  0.012738  0.034135\n","9     0.035057  0.070828  0.013465              0.026923              0.018504  0.065943  0.466793  0.017598  0.019858           0.077036                   0.022105  0.080411            0.020042                  0.019362  0.012459  0.033616\n","10    0.033712  0.070835  0.026364              0.029914              0.018002  0.065897  0.447416  0.020177  0.018970           0.073657                   0.026243  0.078332            0.019219                  0.018602  0.017169  0.035489\n","11    0.033478  0.071708  0.026370              0.030186              0.018221  0.068420  0.442932  0.020426  0.019141           0.073189                   0.027585  0.077776            0.019160                  0.019134  0.017130  0.035145\n","12    0.032234  0.069184  0.033481              0.032271              0.017925  0.065887  0.426731  0.030741  0.019488           0.071679                   0.029084  0.075551            0.018449                  0.018462  0.019145  0.039687\n","13    0.032659  0.068351  0.033131              0.031894              0.019557  0.065095  0.423576  0.031168  0.019574           0.071350                   0.029628  0.074668            0.018472                  0.020282  0.019208  0.041387\n","14    0.033272  0.069810  0.032950              0.031684              0.019424  0.065503  0.420617  0.031240  0.019755           0.070881                   0.029455  0.074424            0.020360                  0.020157  0.019190  0.041276\n","15    0.032947  0.069809  0.036364              0.031521              0.019098  0.071956  0.413657  0.030894  0.019420           0.070709                   0.029634  0.073204            0.020202                  0.021035  0.018953  0.040598\n","16    0.032905  0.070449  0.036438              0.031667              0.019420  0.071352  0.410038  0.030616  0.019461           0.070895                   0.032000  0.072548            0.020129                  0.022399  0.018920  0.040764\n","17    0.032759  0.070105  0.036956              0.031572              0.019709  0.071168  0.408034  0.031347  0.019379           0.071075                   0.031852  0.072518            0.020595                  0.022770  0.019338  0.040823\n","18    0.032649  0.070116  0.037935              0.031778              0.019804  0.070831  0.406150  0.031280  0.019461           0.070761                   0.031898  0.072189            0.020507                  0.022687  0.020016  0.041938\n","19    0.032532  0.069791  0.037831              0.031611              0.019708  0.070619  0.407314  0.031583  0.019681           0.070706                   0.031735  0.071822            0.020399                  0.022742  0.019949  0.041979\n","20    0.032666  0.069539  0.037665              0.031476              0.019666  0.070712  0.405898  0.031567  0.019588           0.070518                   0.031855  0.071677            0.022103                  0.023232  0.020034  0.041803\n","21    0.032926  0.069986  0.038202              0.031499              0.019699  0.070704  0.404500  0.031790  0.019526           0.070275                   0.031748  0.071713            0.022372                  0.023158  0.019966  0.041938\n","22    0.032890  0.070459  0.038151              0.031439              0.019678  0.070655  0.403874  0.031764  0.019500           0.070153                   0.031901  0.071811            0.022329                  0.023201  0.020014  0.042182\n","23    0.032879  0.070376  0.038393              0.031481              0.019696  0.070554  0.403268  0.031741  0.019477           0.070084                   0.032120  0.072182            0.022411                  0.023170  0.019997  0.042171\n","24    0.032840  0.070429  0.038639              0.031622              0.019656  0.070440  0.402453  0.032555  0.019510           0.069929                   0.032099  0.072046            0.022385                  0.023274  0.019968  0.042154\n","25    0.032917  0.070454  0.038705              0.031918              0.019727  0.070592  0.401758  0.032568  0.019477           0.069966                   0.032285  0.071974            0.022347                  0.023236  0.019944  0.042131\n","26    0.032899  0.070320  0.039034              0.032027              0.019745  0.070411  0.400770  0.032657  0.019466           0.070185                   0.032248  0.072513            0.022291                  0.023193  0.020195  0.042049\n","27    0.032966  0.070205  0.038981              0.032159              0.019728  0.070485  0.400275  0.032724  0.019503           0.070167                   0.032280  0.072486            0.022250                  0.023150  0.020239  0.042402\n","28    0.032940  0.070162  0.039074              0.032132              0.019937  0.070579  0.399939  0.032746  0.019490           0.070143                   0.032257  0.072537            0.022251                  0.023181  0.020262  0.042371\n","29    0.032951  0.070160  0.039062              0.032163              0.019931  0.070677  0.399561  0.032737  0.019484           0.070077                   0.032294  0.072606            0.022351                  0.023234  0.020323  0.042389\n","30    0.032919  0.070498  0.039095              0.032165              0.020095  0.070781  0.399154  0.032718  0.019482           0.070039                   0.032261  0.072588            0.022341                  0.023210  0.020307  0.042345\n","31    0.032905  0.070480  0.039049              0.032575              0.020126  0.070702  0.398690  0.032814  0.019466           0.070014                   0.032294  0.072680            0.022320                  0.023268  0.020292  0.042327\n","32    0.032941  0.070448  0.039058              0.032691              0.020171  0.070674  0.398472  0.032842  0.019477           0.069976                   0.032332  0.072673            0.022395                  0.023258  0.020288  0.042304\n","33    0.032953  0.070467  0.039103              0.032732              0.020310  0.070753  0.398222  0.032844  0.019540           0.069937                   0.032303  0.072630            0.022376                  0.023238  0.020271  0.042321\n","34    0.032939  0.070471  0.039350              0.032782              0.020394  0.070703  0.397712  0.032812  0.019618           0.069954                   0.032332  0.072768            0.022348                  0.023247  0.020246  0.042323\n","35    0.032924  0.070430  0.039345              0.032875              0.020434  0.070694  0.397400  0.032810  0.019603           0.069939                   0.032420  0.072748            0.022506                  0.023295  0.020287  0.042290\n","36    0.032929  0.070392  0.039323              0.032954              0.020440  0.070813  0.397174  0.032834  0.019681           0.069902                   0.032432  0.072745            0.022510                  0.023306  0.020287  0.042278\n","37    0.032918  0.070358  0.039314              0.032972              0.020458  0.070792  0.396973  0.032835  0.019796           0.069872                   0.032476  0.072776            0.022588                  0.023308  0.020281  0.042282\n","38    0.032917  0.070357  0.039308              0.032964              0.020482  0.070783  0.396875  0.032830  0.019796           0.069861                   0.032528  0.072767            0.022671                  0.023305  0.020282  0.042275\n","39    0.032905  0.070381  0.039311              0.033027              0.020489  0.070780  0.396699  0.032864  0.019842           0.069916                   0.032519  0.072769            0.022663                  0.023295  0.020273  0.042268\n","40    0.032900  0.070404  0.039345              0.033011              0.020478  0.070755  0.396497  0.032851  0.019889           0.069924                   0.032505  0.072806            0.022768                  0.023301  0.020270  0.042295\n","41    0.032889  0.070384  0.039339              0.033018              0.020501  0.070747  0.396374  0.032842  0.019891           0.069901                   0.032592  0.072787            0.022878                  0.023305  0.020270  0.042282\n","42    0.032878  0.070359  0.039368              0.033040              0.020497  0.070763  0.396234  0.032835  0.019984           0.069922                   0.032637  0.072784            0.022871                  0.023299  0.020262  0.042269\n","43    0.032865  0.070358  0.039465              0.033026              0.020490  0.070743  0.396078  0.032821  0.020050           0.069897                   0.032685  0.072754            0.022955                  0.023289  0.020253  0.042270\n","44    0.032860  0.070345  0.039455              0.033017              0.020486  0.070749  0.395977  0.032827  0.020048           0.069885                   0.032808  0.072752            0.022996                  0.023283  0.020249  0.042263\n","45    0.032847  0.070360  0.039520              0.033020              0.020478  0.070730  0.395853  0.032855  0.020116           0.069873                   0.032799  0.072755            0.022987                  0.023291  0.020242  0.042273\n","46    0.032854  0.070361  0.039573              0.033011              0.020475  0.070715  0.395745  0.032847  0.020151           0.069853                   0.032798  0.072738            0.023058                  0.023288  0.020236  0.042297\n","47    0.032847  0.070346  0.039576              0.033007              0.020471  0.070715  0.395680  0.032841  0.020176           0.069853                   0.032843  0.072723            0.023098                  0.023297  0.020236  0.042291\n","\n","FEVD for M2_US\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.000170  0.013587  0.014622              0.002785              0.022567  0.002905  0.004985  0.938381  0.000000           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.007700  0.011836  0.015087              0.012937              0.037593  0.028731  0.007875  0.807425  0.000545           0.004526                   0.025841  0.019242            0.011409                  0.000764  0.001884  0.006605\n","2     0.009551  0.016086  0.017842              0.024699              0.060707  0.042041  0.022567  0.634091  0.001791           0.038424                   0.019881  0.067273            0.008778                  0.001199  0.028565  0.006505\n","3     0.024831  0.015931  0.022696              0.023601              0.065607  0.040861  0.026189  0.588862  0.009796           0.035681                   0.028838  0.065841            0.009977                  0.003891  0.030500  0.006900\n","4     0.025493  0.014975  0.023548              0.022960              0.064948  0.038719  0.031689  0.555841  0.012228           0.041588                   0.032012  0.065653            0.019989                  0.003726  0.033880  0.012751\n","5     0.027733  0.015174  0.025922              0.022936              0.064303  0.036912  0.030091  0.529299  0.015190           0.040594                   0.031409  0.062100            0.023755                  0.003532  0.035809  0.035238\n","6     0.026388  0.015112  0.026613              0.021970              0.062354  0.040991  0.034387  0.505938  0.017853           0.038911                   0.041393  0.060855            0.025374                  0.003469  0.044560  0.033831\n","7     0.029917  0.015976  0.025926              0.021324              0.061609  0.038917  0.048065  0.480847  0.017065           0.041180                   0.046956  0.064500            0.024583                  0.003973  0.045357  0.033805\n","8     0.029595  0.016089  0.026175              0.025562              0.060811  0.039129  0.050244  0.472177  0.017138           0.040549                   0.048050  0.064192            0.027169                  0.005354  0.044534  0.033234\n","9     0.029817  0.016053  0.025812              0.025363              0.060414  0.038817  0.052390  0.468383  0.017027           0.041344                   0.048332  0.065186            0.027210                  0.006320  0.043949  0.033583\n","10    0.030473  0.018315  0.026393              0.026126              0.059747  0.038924  0.053630  0.463038  0.016879           0.041813                   0.048045  0.064721            0.028986                  0.006240  0.043453  0.033219\n","11    0.030561  0.019685  0.026394              0.025936              0.059446  0.039600  0.053372  0.462386  0.017136           0.041954                   0.047693  0.064368            0.028920                  0.006201  0.043353  0.032996\n","12    0.030114  0.020361  0.026460              0.026293              0.060566  0.039547  0.052555  0.454544  0.020500           0.041387                   0.046897  0.063471            0.034052                  0.007867  0.042638  0.032748\n","13    0.029843  0.023896  0.026233              0.026083              0.061395  0.039274  0.052124  0.450488  0.020489           0.041195                   0.046864  0.064048            0.033922                  0.009146  0.042259  0.032740\n","14    0.029684  0.024578  0.026069              0.025912              0.060858  0.041112  0.052257  0.447327  0.020355           0.041152                   0.046836  0.065456            0.033666                  0.010075  0.041965  0.032699\n","15    0.029458  0.024635  0.027228              0.025802              0.061592  0.041366  0.051860  0.445137  0.020278           0.041719                   0.047912  0.064959            0.033517                  0.010227  0.041647  0.032661\n","16    0.029281  0.024502  0.027100              0.026195              0.061342  0.044044  0.051591  0.442543  0.020382           0.041469                   0.047642  0.064569            0.034611                  0.010514  0.041733  0.032484\n","17    0.029546  0.024437  0.027347              0.027003              0.061211  0.045178  0.051407  0.440991  0.020409           0.041340                   0.047568  0.064350            0.034595                  0.010490  0.041669  0.032459\n","18    0.029427  0.024430  0.027255              0.027058              0.060982  0.045243  0.051214  0.439929  0.020336           0.041183                   0.047640  0.066343            0.034605                  0.010448  0.041502  0.032404\n","19    0.029449  0.024460  0.027189              0.027148              0.060794  0.045089  0.051049  0.438529  0.020362           0.042081                   0.047699  0.066156            0.035114                  0.010680  0.041393  0.032807\n","20    0.029355  0.024673  0.027288              0.027529              0.061311  0.044924  0.050989  0.436902  0.020783           0.041948                   0.047616  0.066202            0.035183                  0.011206  0.041400  0.032691\n","21    0.029534  0.024643  0.027380              0.027561              0.061205  0.044998  0.050814  0.436992  0.020712           0.041807                   0.047452  0.066028            0.035335                  0.011690  0.041263  0.032587\n","22    0.029515  0.024601  0.028025              0.027520              0.061085  0.044899  0.050946  0.436277  0.020949           0.041807                   0.047460  0.065893            0.035362                  0.011836  0.041282  0.032544\n","23    0.029538  0.024778  0.028034              0.027603              0.061120  0.044938  0.050921  0.435892  0.020931           0.041768                   0.047580  0.065868            0.035380                  0.011828  0.041304  0.032518\n","24    0.029547  0.024763  0.028018              0.027646              0.061146  0.044973  0.051025  0.435658  0.020926           0.041848                   0.047551  0.065835            0.035385                  0.011887  0.041288  0.032506\n","25    0.029563  0.024761  0.028026              0.027654              0.061095  0.045073  0.050988  0.435401  0.020913           0.042024                   0.047657  0.065783            0.035414                  0.011880  0.041287  0.032480\n","26    0.029547  0.024793  0.028017              0.027688              0.061075  0.045073  0.051033  0.435179  0.020915           0.042078                   0.047686  0.065759            0.035454                  0.011920  0.041317  0.032465\n","27    0.029511  0.024806  0.028061              0.027653              0.061016  0.045011  0.051102  0.434580  0.021025           0.042019                   0.047642  0.065694            0.035584                  0.012514  0.041361  0.032420\n","28    0.029559  0.024794  0.028214              0.027842              0.060975  0.044993  0.051082  0.434351  0.021043           0.041992                   0.047626  0.065653            0.035573                  0.012518  0.041337  0.032447\n","29    0.029542  0.024848  0.028208              0.027863              0.060936  0.044978  0.051126  0.434113  0.021043           0.041963                   0.047789  0.065662            0.035570                  0.012541  0.041313  0.032505\n","30    0.029538  0.024852  0.028199              0.027880              0.060933  0.045023  0.051114  0.433962  0.021130           0.041953                   0.047854  0.065639            0.035590                  0.012542  0.041298  0.032494\n","31    0.029566  0.024841  0.028227              0.027875              0.060907  0.045060  0.051095  0.433854  0.021123           0.041940                   0.047929  0.065612            0.035660                  0.012548  0.041282  0.032480\n","32    0.029578  0.024831  0.028319              0.027875              0.060886  0.045054  0.051107  0.433729  0.021128           0.041985                   0.047911  0.065595            0.035669                  0.012560  0.041292  0.032479\n","33    0.029583  0.024820  0.028444              0.027933              0.060857  0.045044  0.051121  0.433529  0.021127           0.041986                   0.047905  0.065568            0.035657                  0.012563  0.041312  0.032552\n","34    0.029571  0.024813  0.028455              0.028230              0.060835  0.045029  0.051106  0.433376  0.021119           0.041969                   0.047891  0.065543            0.035648                  0.012565  0.041296  0.032552\n","35    0.029579  0.024810  0.028449              0.028228              0.060848  0.045020  0.051098  0.433366  0.021155           0.041960                   0.047912  0.065528            0.035641                  0.012570  0.041292  0.032546\n","36    0.029578  0.024829  0.028444              0.028238              0.060843  0.045023  0.051094  0.433290  0.021151           0.041979                   0.047948  0.065534            0.035638                  0.012570  0.041285  0.032555\n","37    0.029579  0.024834  0.028538              0.028232              0.060829  0.045037  0.051087  0.433179  0.021149           0.041978                   0.047977  0.065521            0.035628                  0.012589  0.041298  0.032546\n","38    0.029574  0.024881  0.028549              0.028340              0.060821  0.045023  0.051080  0.433047  0.021142           0.041999                   0.047970  0.065504            0.035624                  0.012615  0.041285  0.032545\n","39    0.029586  0.024880  0.028554              0.028351              0.060834  0.045020  0.051080  0.433012  0.021152           0.041996                   0.047965  0.065498            0.035632                  0.012615  0.041282  0.032544\n","40    0.029588  0.024887  0.028581              0.028352              0.060849  0.045018  0.051075  0.432960  0.021149           0.042009                   0.047960  0.065510            0.035629                  0.012615  0.041278  0.032540\n","41    0.029588  0.024899  0.028578              0.028348              0.060853  0.045032  0.051069  0.432910  0.021147           0.042009                   0.047976  0.065538            0.035626                  0.012613  0.041277  0.032537\n","42    0.029597  0.024897  0.028581              0.028358              0.060853  0.045029  0.051067  0.432878  0.021149           0.042015                   0.047978  0.065535            0.035629                  0.012625  0.041274  0.032534\n","43    0.029597  0.024904  0.028617              0.028372              0.060846  0.045034  0.051062  0.432830  0.021150           0.042011                   0.047991  0.065533            0.035626                  0.012624  0.041269  0.032533\n","44    0.029595  0.024924  0.028617              0.028385              0.060841  0.045035  0.051066  0.432799  0.021150           0.042014                   0.047992  0.065532            0.035624                  0.012623  0.041272  0.032531\n","45    0.029595  0.024927  0.028618              0.028392              0.060859  0.045034  0.051063  0.432769  0.021149           0.042012                   0.047991  0.065531            0.035630                  0.012622  0.041276  0.032530\n","46    0.029593  0.024926  0.028617              0.028391              0.060857  0.045031  0.051066  0.432744  0.021148           0.042028                   0.047997  0.065532            0.035633                  0.012632  0.041277  0.032528\n","47    0.029593  0.024928  0.028617              0.028394              0.060858  0.045031  0.051063  0.432721  0.021148           0.042026                   0.047999  0.065541            0.035643                  0.012632  0.041277  0.032528\n","\n","FEVD for M2_UK\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.183179  0.000074  0.009563              0.011205              0.001750  0.003364  0.000003  0.005004  0.785858           0.000000                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.142289  0.005023  0.007773              0.102354              0.010074  0.004086  0.000077  0.027301  0.611383           0.002017                   0.030239  0.005386            0.000016                  0.007882  0.003612  0.040487\n","2     0.124510  0.012455  0.010487              0.089940              0.018795  0.003887  0.002769  0.050601  0.531609           0.016431                   0.030396  0.004678            0.033380                  0.010095  0.022834  0.037132\n","3     0.113062  0.014869  0.009528              0.084966              0.025185  0.014382  0.006696  0.047493  0.493579           0.019244                   0.028693  0.015677            0.030207                  0.014719  0.048064  0.033636\n","4     0.111037  0.027736  0.009384              0.082676              0.035684  0.013397  0.011425  0.044831  0.451624           0.028620                   0.026312  0.015816            0.027425                  0.020366  0.044457  0.049209\n","5     0.098466  0.025658  0.076758              0.076258              0.042514  0.013448  0.010125  0.045554  0.404049           0.026970                   0.031083  0.014012            0.025329                  0.019287  0.046620  0.043869\n","6     0.095257  0.031909  0.074196              0.079119              0.042061  0.014071  0.012636  0.044914  0.392599           0.026816                   0.030183  0.013631            0.031913                  0.018861  0.048327  0.043507\n","7     0.090613  0.030376  0.069617              0.075759              0.043486  0.027983  0.011701  0.044501  0.373514           0.026772                   0.028094  0.012626            0.031078                  0.030559  0.047244  0.056077\n","8     0.085947  0.028934  0.066856              0.078641              0.041586  0.026924  0.016551  0.042749  0.355769           0.037727                   0.026784  0.015652            0.033409                  0.037870  0.047761  0.056840\n","9     0.082749  0.028929  0.072437              0.077792              0.040171  0.028155  0.017264  0.047007  0.351464           0.037497                   0.025784  0.015111            0.033012                  0.037984  0.046035  0.058610\n","10    0.082353  0.029937  0.072393              0.077607              0.040091  0.029958  0.017694  0.046614  0.348523           0.037201                   0.025678  0.015504            0.034258                  0.037949  0.046063  0.058176\n","11    0.082300  0.029405  0.070652              0.083519              0.040338  0.031025  0.017485  0.062730  0.333155           0.036282                   0.024540  0.014884            0.032724                  0.039153  0.044192  0.057617\n","12    0.083061  0.028965  0.069535              0.081790              0.039454  0.030334  0.017244  0.061461  0.331746           0.035466                   0.032897  0.015568            0.032665                  0.038462  0.044158  0.057195\n","13    0.081860  0.030525  0.069050              0.080645              0.039785  0.031059  0.018035  0.060577  0.326796           0.036944                   0.032500  0.015703            0.034603                  0.038102  0.047472  0.056343\n","14    0.080525  0.030849  0.068651              0.080457              0.039168  0.031018  0.017890  0.060585  0.322227           0.041096                   0.034274  0.019135            0.034257                  0.037727  0.046697  0.055443\n","15    0.079819  0.031834  0.070161              0.080305              0.038828  0.030758  0.018433  0.060183  0.320179           0.041802                   0.034436  0.020100            0.034200                  0.037450  0.046323  0.055189\n","16    0.079535  0.031685  0.071661              0.080253              0.038554  0.030962  0.018737  0.060327  0.317767           0.042663                   0.034882  0.020026            0.033909                  0.037148  0.046172  0.055718\n","17    0.078933  0.031418  0.071104              0.079611              0.038291  0.031500  0.020362  0.060502  0.316311           0.042620                   0.036389  0.020050            0.034684                  0.037125  0.045804  0.055296\n","18    0.078411  0.031428  0.070905              0.078680              0.038805  0.031130  0.020700  0.060867  0.315033           0.045902                   0.036136  0.019913            0.035006                  0.036860  0.045567  0.054657\n","19    0.078109  0.031449  0.070709              0.078392              0.038700  0.031034  0.021171  0.060632  0.313573           0.047119                   0.036120  0.021173            0.034887                  0.036985  0.045544  0.054404\n","20    0.077953  0.031355  0.071587              0.078133              0.038586  0.030947  0.021869  0.060483  0.313018           0.046963                   0.036217  0.021204            0.034824                  0.037139  0.045419  0.054305\n","21    0.077708  0.031271  0.071699              0.077974              0.038435  0.031362  0.021984  0.060247  0.311799           0.047057                   0.036198  0.021202            0.035061                  0.037013  0.045512  0.055479\n","22    0.077560  0.031208  0.071674              0.077828              0.038552  0.031424  0.021975  0.060296  0.311326           0.047054                   0.036714  0.021194            0.034993                  0.036943  0.045880  0.055380\n","23    0.077420  0.031208  0.071532              0.077817              0.038828  0.031361  0.022238  0.060193  0.311087           0.046967                   0.036860  0.021192            0.034933                  0.037130  0.045792  0.055442\n","24    0.077280  0.031703  0.071890              0.077782              0.038775  0.031390  0.022202  0.060098  0.310590           0.046992                   0.036946  0.021243            0.034874                  0.037084  0.045800  0.055351\n","25    0.077091  0.031845  0.071922              0.077732              0.038928  0.031498  0.022176  0.060475  0.309919           0.047405                   0.036918  0.021199            0.034795                  0.037040  0.045754  0.055305\n","26    0.076991  0.031797  0.072306              0.077602              0.038901  0.031472  0.022355  0.060385  0.309439           0.047336                   0.037010  0.021203            0.035004                  0.037149  0.045683  0.055368\n","27    0.076876  0.031749  0.072909              0.077536              0.038956  0.031498  0.022356  0.060512  0.308992           0.047316                   0.037083  0.021201            0.034953                  0.037101  0.045674  0.055289\n","28    0.076787  0.031925  0.072818              0.077460              0.039325  0.031452  0.022410  0.060418  0.308518           0.047245                   0.037194  0.021402            0.034982                  0.037095  0.045650  0.055319\n","29    0.076664  0.031892  0.072883              0.077469              0.039266  0.031402  0.022442  0.060673  0.308016           0.047429                   0.037260  0.021610            0.034972                  0.037085  0.045616  0.055322\n","30    0.076601  0.031861  0.072842              0.077867              0.039266  0.031406  0.022422  0.060621  0.307816           0.047390                   0.037265  0.021589            0.035057                  0.037063  0.045573  0.055361\n","31    0.076523  0.031897  0.072791              0.077896              0.039228  0.031402  0.022526  0.060594  0.307693           0.047341                   0.037386  0.021707            0.035069                  0.037058  0.045574  0.055316\n","32    0.076466  0.031886  0.072847              0.077824              0.039220  0.031573  0.022696  0.060532  0.307389           0.047339                   0.037509  0.021685            0.035089                  0.037021  0.045585  0.055338\n","33    0.076464  0.031931  0.072967              0.077869              0.039310  0.031586  0.022705  0.060485  0.307246           0.047311                   0.037486  0.021694            0.035079                  0.036996  0.045549  0.055322\n","34    0.076436  0.031919  0.072947              0.077825              0.039344  0.031610  0.022693  0.060501  0.307164           0.047292                   0.037512  0.021797            0.035098                  0.037025  0.045548  0.055289\n","35    0.076367  0.031902  0.072884              0.077764              0.039311  0.031738  0.022820  0.060449  0.306898           0.047386                   0.037635  0.021875            0.035226                  0.036999  0.045506  0.055241\n","36    0.076338  0.031910  0.072859              0.077903              0.039291  0.031753  0.022867  0.060405  0.306832           0.047375                   0.037610  0.021964            0.035211                  0.036973  0.045490  0.055218\n","37    0.076323  0.031902  0.072840              0.077920              0.039281  0.031772  0.022862  0.060391  0.306832           0.047364                   0.037636  0.021973            0.035241                  0.036982  0.045478  0.055204\n","38    0.076285  0.031912  0.072834              0.077883              0.039306  0.031822  0.022883  0.060364  0.306685           0.047343                   0.037774  0.021969            0.035322                  0.036963  0.045467  0.055188\n","39    0.076231  0.031986  0.072873              0.077927              0.039339  0.031833  0.022893  0.060326  0.306510           0.047338                   0.037749  0.022170            0.035292                  0.036930  0.045430  0.055173\n","40    0.076189  0.032052  0.072848              0.077881              0.039315  0.031821  0.022882  0.060297  0.306398           0.047316                   0.037821  0.022288            0.035400                  0.036933  0.045418  0.055139\n","41    0.076165  0.032043  0.072821              0.077889              0.039301  0.031859  0.022911  0.060269  0.306314           0.047298                   0.037925  0.022279            0.035490                  0.036926  0.045398  0.055113\n","42    0.076123  0.032068  0.072818              0.078001              0.039287  0.031890  0.022911  0.060244  0.306313           0.047297                   0.037904  0.022297            0.035472                  0.036907  0.045374  0.055094\n","43    0.076102  0.032068  0.072791              0.077985              0.039277  0.031878  0.022919  0.060247  0.306265           0.047285                   0.037946  0.022340            0.035541                  0.036900  0.045368  0.055088\n","44    0.076069  0.032063  0.072766              0.077955              0.039279  0.031909  0.022967  0.060239  0.306147           0.047270                   0.038071  0.022337            0.035616                  0.036889  0.045350  0.055074\n","45    0.076031  0.032068  0.072824              0.077943              0.039261  0.031913  0.022992  0.060216  0.306130           0.047278                   0.038070  0.022380            0.035598                  0.036870  0.045329  0.055098\n","46    0.076005  0.032073  0.072867              0.077919              0.039248  0.031902  0.022991  0.060195  0.306045           0.047262                   0.038133  0.022411            0.035676                  0.036869  0.045313  0.055093\n","47    0.075978  0.032069  0.072839              0.077919              0.039234  0.031949  0.023007  0.060198  0.305965           0.047253                   0.038208  0.022406            0.035730                  0.036876  0.045295  0.055074\n","\n","FEVD for Stock Market News\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.001215  0.043376  0.000116              0.000516              0.009100  0.000006  0.002300  0.000999  0.006585           0.935786                   0.000000  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.001296  0.042984  0.009685              0.022989              0.008469  0.021880  0.012438  0.012512  0.006050           0.819333                   0.005452  0.000595            0.002411                  0.023947  0.001205  0.008754\n","2     0.002157  0.044002  0.015634              0.021452              0.010329  0.037095  0.036463  0.023445  0.009519           0.736687                   0.008705  0.012050            0.004766                  0.024396  0.005305  0.007996\n","3     0.002260  0.041505  0.042230              0.023548              0.010382  0.034307  0.038346  0.036120  0.022992           0.682015                   0.008320  0.012821            0.004429                  0.026324  0.006567  0.007837\n","4     0.007223  0.038054  0.040932              0.063761              0.013123  0.037670  0.038332  0.033106  0.023097           0.624429                   0.019809  0.011697            0.004402                  0.025190  0.006937  0.012237\n","5     0.007021  0.037496  0.048707              0.060561              0.013464  0.035889  0.038103  0.031871  0.022056           0.593152                   0.021038  0.011553            0.008626                  0.033215  0.015632  0.021617\n","6     0.006947  0.038253  0.054725              0.063639              0.014472  0.049198  0.036115  0.039345  0.020958           0.562212                   0.020556  0.010943            0.008171                  0.034033  0.015573  0.024861\n","7     0.007448  0.045976  0.052839              0.062372              0.033099  0.049266  0.034191  0.040517  0.020232           0.530187                   0.022136  0.014111            0.007689                  0.036159  0.014651  0.029127\n","8     0.012378  0.044698  0.050462              0.068881              0.031457  0.052478  0.035446  0.038879  0.019582           0.509098                   0.029684  0.014985            0.007833                  0.042342  0.013977  0.027821\n","9     0.013752  0.045540  0.049579              0.068955              0.032383  0.051543  0.035199  0.041337  0.022601           0.501086                   0.029528  0.014712            0.010051                  0.042400  0.013780  0.027556\n","10    0.013859  0.044685  0.050598              0.067632              0.034243  0.052514  0.034561  0.040782  0.023820           0.497633                   0.029172  0.017321            0.010766                  0.041622  0.013715  0.027077\n","11    0.013844  0.044745  0.052983              0.067296              0.034256  0.051840  0.034215  0.041551  0.023551           0.491369                   0.031348  0.018306            0.013010                  0.041124  0.013591  0.026970\n","12    0.014270  0.045544  0.052596              0.066750              0.034460  0.052875  0.034438  0.043060  0.023328           0.487757                   0.032742  0.018115            0.012861                  0.040652  0.013536  0.027017\n","13    0.014564  0.044953  0.056515              0.065922              0.034278  0.052154  0.034440  0.042767  0.023241           0.481502                   0.035622  0.019185            0.014575                  0.040033  0.013645  0.026607\n","14    0.014355  0.045205  0.057962              0.065143              0.033827  0.052125  0.037570  0.042251  0.023862           0.472632                   0.038127  0.018874            0.014306                  0.041134  0.015259  0.027367\n","15    0.014482  0.045370  0.059247              0.064655              0.034163  0.052838  0.037772  0.043322  0.023702           0.469552                   0.037877  0.018771            0.015112                  0.040831  0.015145  0.027161\n","16    0.014404  0.047509  0.059147              0.064507              0.033975  0.052625  0.037795  0.043162  0.023624           0.467058                   0.037808  0.018669            0.015058                  0.041660  0.015080  0.027919\n","17    0.014511  0.047612  0.059088              0.064742              0.033884  0.052453  0.037724  0.043026  0.023629           0.465956                   0.037779  0.018616            0.015040                  0.042948  0.015136  0.027854\n","18    0.014512  0.048514  0.059203              0.064343              0.033692  0.052124  0.038064  0.042758  0.024236           0.463150                   0.038970  0.019237            0.015023                  0.043352  0.015143  0.027679\n","19    0.014577  0.048444  0.059393              0.064333              0.033646  0.053359  0.037941  0.042564  0.024703           0.460973                   0.038775  0.020020            0.015358                  0.043224  0.015116  0.027575\n","20    0.014543  0.048332  0.059237              0.064158              0.033662  0.053212  0.037857  0.042857  0.024756           0.459653                   0.039572  0.020074            0.015418                  0.043245  0.015076  0.028348\n","21    0.014522  0.048589  0.059040              0.064040              0.033704  0.053049  0.038294  0.043246  0.025061           0.458014                   0.039428  0.020664            0.015489                  0.043380  0.015128  0.028352\n","22    0.014544  0.048551  0.059203              0.063939              0.033657  0.052978  0.038663  0.043233  0.025023           0.457286                   0.039579  0.020868            0.015590                  0.043313  0.015142  0.028431\n","23    0.014574  0.048522  0.059553              0.063839              0.033851  0.052885  0.038605  0.043442  0.024967           0.456423                   0.039572  0.020835            0.015787                  0.043220  0.015271  0.028653\n","24    0.014577  0.048493  0.059888              0.063748              0.033803  0.052895  0.038592  0.043381  0.024995           0.455883                   0.039939  0.020841            0.015770                  0.043182  0.015287  0.028726\n","25    0.014618  0.049077  0.060001              0.063760              0.034206  0.053211  0.038504  0.043292  0.024937           0.454847                   0.039869  0.020900            0.015736                  0.043080  0.015304  0.028658\n","26    0.014631  0.049512  0.060098              0.063945              0.034322  0.053209  0.038403  0.043156  0.024858           0.453396                   0.040735  0.021195            0.015719                  0.042993  0.015262  0.028567\n","27    0.014658  0.049472  0.060081              0.063905              0.034292  0.053352  0.038403  0.043118  0.024982           0.453131                   0.040801  0.021255            0.015715                  0.042957  0.015323  0.028554\n","28    0.014683  0.049532  0.060379              0.064062              0.034474  0.053277  0.038346  0.043074  0.024939           0.452328                   0.040778  0.021236            0.015771                  0.042920  0.015565  0.028636\n","29    0.014700  0.049494  0.060321              0.064033              0.034442  0.053297  0.038311  0.043050  0.025102           0.451906                   0.040739  0.021297            0.015804                  0.043186  0.015551  0.028767\n","30    0.014783  0.049522  0.060252              0.063977              0.034428  0.053229  0.038352  0.043014  0.025070           0.451340                   0.041073  0.021649            0.015809                  0.043173  0.015534  0.028794\n","31    0.014781  0.049468  0.060204              0.064292              0.034404  0.053282  0.038311  0.043104  0.025332           0.450812                   0.041056  0.021629            0.015793                  0.043123  0.015553  0.028854\n","32    0.014783  0.049444  0.060190              0.064251              0.034382  0.053317  0.038301  0.043069  0.025372           0.450477                   0.041168  0.021777            0.015838                  0.043188  0.015543  0.028899\n","33    0.014780  0.049504  0.060163              0.064234              0.034378  0.053395  0.038308  0.043072  0.025382           0.450283                   0.041205  0.021794            0.015918                  0.043166  0.015535  0.028884\n","34    0.014805  0.049578  0.060248              0.064230              0.034463  0.053376  0.038310  0.043043  0.025448           0.449958                   0.041185  0.021828            0.015915                  0.043151  0.015532  0.028930\n","35    0.014797  0.049593  0.060291              0.064195              0.034461  0.053351  0.038297  0.043021  0.025522           0.449701                   0.041169  0.021822            0.016173                  0.043142  0.015544  0.028920\n","36    0.014796  0.049568  0.060266              0.064162              0.034450  0.053335  0.038279  0.043030  0.025509           0.449475                   0.041476  0.021816            0.016218                  0.043121  0.015590  0.028908\n","37    0.014785  0.049696  0.060391              0.064137              0.034466  0.053418  0.038292  0.043078  0.025557           0.449112                   0.041452  0.021834            0.016212                  0.043086  0.015582  0.028900\n","38    0.014777  0.049716  0.060414              0.064094              0.034446  0.053442  0.038284  0.043049  0.025623           0.448800                   0.041591  0.021938            0.016251                  0.043068  0.015597  0.028912\n","39    0.014782  0.049705  0.060403              0.064107              0.034437  0.053467  0.038280  0.043056  0.025626           0.448619                   0.041694  0.021931            0.016340                  0.043055  0.015598  0.028901\n","40    0.014774  0.049729  0.060443              0.064105              0.034419  0.053474  0.038283  0.043032  0.025764           0.448397                   0.041675  0.022008            0.016334                  0.043031  0.015608  0.028924\n","41    0.014782  0.049750  0.060481              0.064090              0.034407  0.053450  0.038271  0.043018  0.025807           0.448211                   0.041702  0.022036            0.016433                  0.043012  0.015601  0.028949\n","42    0.014778  0.049731  0.060483              0.064074              0.034393  0.053441  0.038286  0.043008  0.025816           0.448072                   0.041828  0.022033            0.016504                  0.043011  0.015599  0.028942\n","43    0.014782  0.049742  0.060491              0.064064              0.034378  0.053457  0.038303  0.042991  0.026073           0.447822                   0.041813  0.022027            0.016497                  0.042992  0.015596  0.028972\n","44    0.014776  0.049750  0.060535              0.064059              0.034371  0.053441  0.038297  0.042989  0.026086           0.447645                   0.041876  0.022063            0.016566                  0.042983  0.015590  0.028975\n","45    0.014771  0.049733  0.060511              0.064039              0.034360  0.053472  0.038342  0.042989  0.026124           0.447467                   0.041939  0.022054            0.016652                  0.043001  0.015584  0.028964\n","46    0.014765  0.049752  0.060569              0.064033              0.034359  0.053508  0.038351  0.042975  0.026181           0.447312                   0.041945  0.022053            0.016647                  0.042986  0.015580  0.028984\n","47    0.014762  0.049744  0.060602              0.064044              0.034351  0.053494  0.038341  0.042963  0.026207           0.447188                   0.041961  0.022050            0.016736                  0.042981  0.015576  0.028999\n","\n","FEVD for Economic Development News\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.004845  0.007905  0.000082              0.001545              0.016048  0.000126  0.001254  0.000007  0.000932           0.017987                   0.949268  0.000000            0.000000                  0.000000  0.000000  0.000000\n","1     0.003892  0.019785  0.010708              0.001079              0.049008  0.025294  0.003108  0.000066  0.010237           0.014635                   0.838211  0.000225            0.000408                  0.005771  0.002502  0.015072\n","2     0.003943  0.017230  0.012558              0.012972              0.062420  0.043816  0.004776  0.004397  0.022560           0.020046                   0.749589  0.001727            0.005934                  0.016151  0.004770  0.017110\n","3     0.003580  0.017945  0.016491              0.011842              0.063448  0.040600  0.014187  0.003882  0.020428           0.022257                   0.662175  0.004495            0.041463                  0.014277  0.040470  0.022462\n","4     0.007378  0.019904  0.043820              0.021853              0.065117  0.035679  0.016299  0.005737  0.025509           0.020712                   0.588207  0.015182            0.054459                  0.019182  0.041315  0.019647\n","5     0.007777  0.022326  0.044252              0.034362              0.062263  0.034706  0.018122  0.009784  0.024669           0.021800                   0.562402  0.014529            0.063779                  0.018684  0.041272  0.019273\n","6     0.008408  0.021354  0.045855              0.035485              0.061381  0.033285  0.023202  0.013877  0.025138           0.020910                   0.540111  0.014027            0.075899                  0.022221  0.040195  0.018652\n","7     0.014186  0.020554  0.045865              0.034314              0.059752  0.033402  0.022509  0.013367  0.027997           0.021216                   0.526778  0.013970            0.084471                  0.021899  0.039765  0.019955\n","8     0.023387  0.025513  0.046369              0.032337              0.065750  0.031976  0.032186  0.012596  0.026542           0.020172                   0.496155  0.029776            0.079481                  0.020612  0.037422  0.019726\n","9     0.023780  0.026539  0.045037              0.032424              0.064264  0.037236  0.034795  0.013185  0.025738           0.020926                   0.485599  0.029096            0.085245                  0.020870  0.036174  0.019093\n","10    0.023316  0.026041  0.044517              0.032353              0.067167  0.039735  0.034353  0.012910  0.025954           0.020926                   0.479902  0.029512            0.084197                  0.023172  0.035645  0.020299\n","11    0.025413  0.025361  0.043812              0.036593              0.065921  0.039368  0.033428  0.013608  0.034746           0.022287                   0.465993  0.028810            0.084168                  0.022836  0.036909  0.020747\n","12    0.025944  0.024515  0.043982              0.035761              0.064288  0.038360  0.037365  0.013190  0.035621           0.021879                   0.462061  0.028380            0.085121                  0.026849  0.035951  0.020734\n","13    0.025370  0.028256  0.043171              0.039999              0.063250  0.038809  0.041265  0.012896  0.037748           0.021387                   0.449547  0.027679            0.084979                  0.029809  0.035670  0.020164\n","14    0.024866  0.028080  0.042164              0.046222              0.062311  0.037895  0.040944  0.013238  0.043491           0.020914                   0.441901  0.029223            0.083217                  0.029925  0.035805  0.019804\n","15    0.024334  0.027602  0.041253              0.046658              0.061065  0.039987  0.040790  0.014559  0.042771           0.021630                   0.433306  0.028595            0.092004                  0.030698  0.035125  0.019623\n","16    0.024413  0.027127  0.041569              0.046980              0.060254  0.043913  0.040511  0.014314  0.043449           0.021429                   0.426649  0.029774            0.094125                  0.030425  0.035338  0.019729\n","17    0.024284  0.028023  0.043056              0.046752              0.059906  0.044319  0.040002  0.014183  0.045255           0.022353                   0.420479  0.029786            0.095166                  0.030419  0.035437  0.020582\n","18    0.023955  0.027859  0.043134              0.047627              0.059124  0.043777  0.039584  0.014057  0.044678           0.022053                   0.417256  0.029461            0.099775                  0.032110  0.035245  0.020306\n","19    0.023525  0.027725  0.042384              0.052584              0.058455  0.044524  0.038885  0.014301  0.047713           0.022514                   0.413142  0.029377            0.098482                  0.031823  0.034625  0.019941\n","20    0.023177  0.030652  0.042280              0.054241              0.057621  0.044526  0.038553  0.014109  0.049443           0.022183                   0.407178  0.031441            0.099293                  0.031431  0.034129  0.019745\n","21    0.023673  0.030794  0.042119              0.053675              0.057357  0.044454  0.038526  0.014297  0.048872           0.022167                   0.406274  0.031127            0.101451                  0.031608  0.033928  0.019679\n","22    0.024193  0.030627  0.042890              0.053607              0.057069  0.044131  0.038884  0.014311  0.049581           0.023949                   0.403640  0.031501            0.100753                  0.031812  0.033534  0.019518\n","23    0.023888  0.030405  0.045853              0.054205              0.056362  0.043570  0.038702  0.014138  0.052803           0.024557                   0.398550  0.031397            0.100732                  0.031412  0.033124  0.020302\n","24    0.023686  0.030216  0.046003              0.053758              0.055997  0.043333  0.039053  0.014290  0.052348           0.024347                   0.397668  0.031375            0.103156                  0.031798  0.032791  0.020182\n","25    0.023467  0.030073  0.045970              0.054723              0.055360  0.043636  0.040080  0.014657  0.055847           0.024076                   0.394300  0.031098            0.102221                  0.031806  0.032419  0.020266\n","26    0.023378  0.030446  0.046906              0.054876              0.054948  0.043732  0.039821  0.014553  0.058507           0.023952                   0.391485  0.030905            0.102021                  0.031727  0.032154  0.020588\n","27    0.023230  0.030269  0.046960              0.054498              0.054565  0.043652  0.040063  0.015024  0.058099           0.023944                   0.390467  0.030911            0.103286                  0.032490  0.031983  0.020559\n","28    0.023113  0.030800  0.047210              0.054453              0.054155  0.044141  0.040497  0.014932  0.059603           0.024138                   0.388365  0.031219            0.102720                  0.032400  0.031749  0.020504\n","29    0.022945  0.031823  0.048816              0.054198              0.053814  0.043940  0.040203  0.014889  0.060188           0.023965                   0.385576  0.031327            0.103635                  0.032431  0.031554  0.020696\n","30    0.022803  0.031866  0.048569              0.053837              0.053480  0.043684  0.040331  0.014812  0.059848           0.024395                   0.385019  0.031158            0.105312                  0.032973  0.031344  0.020567\n","31    0.022678  0.031936  0.048810              0.053919              0.053185  0.043813  0.040487  0.014877  0.060883           0.024521                   0.384216  0.031042            0.104760                  0.033081  0.031171  0.020619\n","32    0.022559  0.032419  0.049637              0.054391              0.053011  0.043884  0.040314  0.014829  0.061690           0.024424                   0.382391  0.031034            0.104797                  0.032927  0.031013  0.020680\n","33    0.022455  0.032292  0.049686              0.054108              0.052744  0.043764  0.040381  0.015159  0.061365           0.024444                   0.382640  0.030883            0.105241                  0.033303  0.030934  0.020600\n","34    0.022347  0.032449  0.050498              0.054377              0.052504  0.043918  0.040647  0.015202  0.061898           0.024442                   0.381338  0.030754            0.104739                  0.033356  0.030770  0.020761\n","35    0.022269  0.032798  0.051789              0.054523              0.052285  0.043718  0.040445  0.015122  0.062525           0.024317                   0.379416  0.030740            0.104799                  0.033314  0.030690  0.021248\n","36    0.022211  0.032713  0.051589              0.054299              0.052068  0.043608  0.040532  0.015172  0.062265           0.024371                   0.379392  0.030678            0.105613                  0.033731  0.030590  0.021170\n","37    0.022112  0.032758  0.051876              0.054636              0.051836  0.043871  0.040781  0.015220  0.063145           0.024271                   0.378281  0.030550            0.105228                  0.033751  0.030454  0.021229\n","38    0.022012  0.033172  0.052443              0.055137              0.051689  0.043968  0.040595  0.015159  0.063753           0.024185                   0.376835  0.030527            0.105078                  0.033707  0.030344  0.021395\n","39    0.021937  0.033078  0.052291              0.054968              0.051564  0.043872  0.040881  0.015362  0.063535           0.024102                   0.376570  0.030467            0.105613                  0.034187  0.030244  0.021328\n","40    0.021863  0.033111  0.052792              0.055054              0.051420  0.044048  0.041133  0.015381  0.064003           0.024010                   0.375683  0.030352            0.105408                  0.034114  0.030153  0.021476\n","41    0.021787  0.033356  0.053572              0.055094              0.051327  0.044054  0.040990  0.015336  0.064480           0.023932                   0.374336  0.030413            0.105459                  0.034187  0.030091  0.021588\n","42    0.021722  0.033336  0.053459              0.054931              0.051181  0.043946  0.040990  0.015323  0.064293           0.023910                   0.374561  0.030394            0.106025                  0.034400  0.030002  0.021527\n","43    0.021667  0.033476  0.053637              0.055125              0.051050  0.044122  0.041090  0.015366  0.064655           0.023843                   0.374048  0.030322            0.105747                  0.034384  0.029926  0.021542\n","44    0.021594  0.033805  0.054042              0.055455              0.050983  0.044195  0.040982  0.015315  0.065117           0.023772                   0.372921  0.030361            0.105646                  0.034323  0.029837  0.021651\n","45    0.021576  0.033737  0.053957              0.055325              0.050859  0.044119  0.041044  0.015377  0.064969           0.023728                   0.372946  0.030370            0.106075                  0.034533  0.029771  0.021613\n","46    0.021539  0.033837  0.054235              0.055492              0.050795  0.044249  0.041225  0.015359  0.065271           0.023679                   0.372276  0.030302            0.105895                  0.034478  0.029689  0.021680\n","47    0.021473  0.034143  0.054728              0.055587              0.050690  0.044242  0.041112  0.015315  0.065763           0.023612                   0.371190  0.030428            0.105836                  0.034470  0.029611  0.021801\n","\n","FEVD for FED News\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.011253  0.013397  0.007543              0.030056              0.029929  0.000942  0.000262  0.001016  0.010757           0.003906                   0.082655  0.808285            0.000000                  0.000000  0.000000  0.000000\n","1     0.013720  0.025031  0.034705              0.035513              0.029556  0.001486  0.001510  0.004238  0.048331           0.003173                   0.068266  0.719579            0.000542                  0.002030  0.011704  0.000615\n","2     0.013760  0.021904  0.047105              0.060889              0.067762  0.006232  0.011422  0.003717  0.045065           0.004176                   0.068944  0.629735            0.005678                  0.002379  0.010235  0.000997\n","3     0.017080  0.020756  0.046019              0.071868              0.077404  0.005903  0.010873  0.004614  0.042295           0.004020                   0.064662  0.604275            0.007638                  0.003082  0.018042  0.001467\n","4     0.022685  0.021590  0.060044              0.064521              0.069577  0.011690  0.024454  0.004275  0.042985           0.004441                   0.080737  0.535972            0.011500                  0.004180  0.038681  0.002670\n","5     0.021090  0.037774  0.064993              0.057550              0.069333  0.043359  0.026274  0.005022  0.039596           0.009809                   0.077114  0.481817            0.012507                  0.016581  0.034524  0.002657\n","6     0.020155  0.035568  0.062035              0.054757              0.066139  0.040874  0.024805  0.005048  0.037396           0.016979                   0.087232  0.466118            0.021052                  0.025372  0.033806  0.002664\n","7     0.018534  0.044406  0.057038              0.052939              0.066762  0.064489  0.022783  0.008984  0.045173           0.025307                   0.081104  0.428130            0.024547                  0.025842  0.031064  0.002897\n","8     0.017554  0.043962  0.055235              0.060870              0.064980  0.068120  0.021618  0.009636  0.044096           0.024744                   0.089659  0.413629            0.023979                  0.027129  0.031714  0.003077\n","9     0.017278  0.043223  0.054757              0.061953              0.063925  0.066765  0.021969  0.009446  0.044199           0.029304                   0.089798  0.408673            0.025063                  0.028864  0.031515  0.003267\n","10    0.016739  0.041782  0.052969              0.062873              0.062975  0.064697  0.022113  0.009153  0.052272           0.028305                   0.093704  0.394667            0.028656                  0.030615  0.030498  0.007984\n","11    0.016059  0.040145  0.060582              0.063167              0.062063  0.067228  0.021949  0.009263  0.059785           0.027617                   0.091442  0.381537            0.029093                  0.030027  0.029341  0.010702\n","12    0.016451  0.039883  0.061445              0.063244              0.060022  0.064993  0.024106  0.009193  0.057417           0.027078                   0.099246  0.371828            0.035182                  0.030261  0.028760  0.010889\n","13    0.016041  0.040630  0.059966              0.065878              0.059269  0.067323  0.023871  0.008937  0.062093           0.026396                   0.101903  0.362428            0.034192                  0.029517  0.030407  0.011149\n","14    0.016128  0.043385  0.061500              0.066606              0.058244  0.066782  0.023914  0.008747  0.063597           0.026630                   0.101248  0.356030            0.037320                  0.028900  0.029824  0.011145\n","15    0.015795  0.042809  0.061784              0.067355              0.057038  0.065554  0.023339  0.008719  0.064359           0.026252                   0.102457  0.349609            0.044987                  0.028124  0.029017  0.012802\n","16    0.017295  0.044424  0.062181              0.066145              0.057716  0.065598  0.023572  0.008551  0.063212           0.027740                   0.102119  0.346452            0.045900                  0.027683  0.028848  0.012562\n","17    0.017071  0.046713  0.064152              0.065206              0.056684  0.064847  0.023234  0.009303  0.066110           0.027736                   0.100299  0.342085            0.047486                  0.027912  0.028419  0.012743\n","18    0.016877  0.047433  0.063686              0.064606              0.055894  0.063793  0.022958  0.009159  0.065279           0.027547                   0.104528  0.337118            0.051054                  0.028507  0.028972  0.012588\n","19    0.017455  0.047021  0.062751              0.064077              0.055045  0.065728  0.022604  0.010514  0.069184           0.027370                   0.105665  0.332362            0.050790                  0.028511  0.028526  0.012396\n","20    0.017649  0.047772  0.063418              0.064077              0.054371  0.065706  0.022417  0.010488  0.072631           0.027451                   0.104997  0.329112            0.050647                  0.028217  0.028254  0.012793\n","21    0.017749  0.047383  0.063183              0.063282              0.053560  0.065272  0.023538  0.011401  0.071797           0.027165                   0.105727  0.324203            0.054116                  0.029558  0.028775  0.013289\n","22    0.017442  0.046539  0.065395              0.063126              0.052895  0.064144  0.024481  0.011194  0.074089           0.029032                   0.106004  0.318313            0.054048                  0.031011  0.028252  0.014035\n","23    0.017568  0.046640  0.067152              0.062976              0.052334  0.063480  0.024233  0.011073  0.075787           0.029238                   0.104982  0.314941            0.055813                  0.030783  0.027950  0.015052\n","24    0.017413  0.046431  0.066602              0.062485              0.051899  0.062920  0.024528  0.011350  0.075120           0.029218                   0.107449  0.312180            0.058662                  0.031096  0.027707  0.014941\n","25    0.017265  0.046422  0.066267              0.062425              0.051300  0.064228  0.025464  0.011861  0.076927           0.028878                   0.108286  0.308537            0.058189                  0.031422  0.027700  0.014829\n","26    0.017370  0.047280  0.069044              0.063029              0.051036  0.064607  0.025363  0.011724  0.077548           0.028585                   0.107699  0.304985            0.058081                  0.031059  0.027381  0.015210\n","27    0.017328  0.046922  0.069258              0.062574              0.050678  0.064654  0.025405  0.011836  0.076976           0.028368                   0.108540  0.302740            0.060378                  0.031536  0.027210  0.015598\n","28    0.017337  0.047256  0.069700              0.062345              0.050440  0.065319  0.025584  0.011769  0.077324           0.028270                   0.108863  0.301116            0.060501                  0.031458  0.027039  0.015680\n","29    0.017210  0.047887  0.070790              0.062622              0.050062  0.065400  0.025386  0.011725  0.078506           0.028066                   0.108050  0.299001            0.061282                  0.031332  0.026835  0.015847\n","30    0.017077  0.048223  0.070586              0.062322              0.049824  0.064923  0.025461  0.011641  0.077933           0.028201                   0.110340  0.296858            0.062329                  0.031909  0.026626  0.015748\n","31    0.016980  0.048073  0.070340              0.062455              0.049521  0.065196  0.025843  0.011977  0.079085           0.028087                   0.110617  0.295018            0.062069                  0.032482  0.026461  0.015797\n","32    0.016872  0.048316  0.071159              0.063060              0.049575  0.065123  0.025693  0.011920  0.080138           0.027914                   0.110071  0.293474            0.061832                  0.032373  0.026404  0.016076\n","33    0.016771  0.048093  0.070965              0.062690              0.049286  0.064790  0.026068  0.012196  0.079665           0.027771                   0.110573  0.291927            0.063450                  0.033421  0.026354  0.015982\n","34    0.016671  0.047854  0.071417              0.062793              0.049180  0.064623  0.026381  0.012125  0.080049           0.027789                   0.111510  0.290182            0.063575                  0.033470  0.026245  0.016135\n","35    0.016592  0.048166  0.072152              0.063147              0.049017  0.064376  0.026273  0.012073  0.080802           0.027648                   0.110928  0.288749            0.064142                  0.033382  0.026181  0.016371\n","36    0.016532  0.048041  0.071863              0.062937              0.048809  0.064094  0.026260  0.012053  0.080504           0.027566                   0.112354  0.287515            0.065270                  0.033770  0.026064  0.016368\n","37    0.016504  0.048215  0.071778              0.063227              0.048654  0.064380  0.026568  0.012269  0.080900           0.027433                   0.112629  0.286134            0.065134                  0.033884  0.025940  0.016351\n","38    0.016433  0.048655  0.072405              0.063691              0.048635  0.064416  0.026477  0.012211  0.081530           0.027289                   0.112093  0.284843            0.065069                  0.033820  0.025829  0.016604\n","39    0.016410  0.048536  0.072358              0.063436              0.048440  0.064210  0.026598  0.012299  0.081229           0.027179                   0.112667  0.283788            0.066246                  0.034249  0.025738  0.016615\n","40    0.016370  0.048464  0.072585              0.063518              0.048331  0.064341  0.026742  0.012277  0.081431           0.027133                   0.113368  0.282621            0.066329                  0.034182  0.025661  0.016647\n","41    0.016295  0.048848  0.073262              0.063619              0.048152  0.064299  0.026654  0.012226  0.082282           0.027011                   0.112866  0.281598            0.066439                  0.034094  0.025557  0.016798\n","42    0.016237  0.048854  0.073169              0.063397              0.047981  0.064058  0.026652  0.012248  0.082018           0.026959                   0.114103  0.280684            0.067147                  0.034271  0.025461  0.016760\n","43    0.016192  0.048834  0.073095              0.063597              0.047810  0.064182  0.026958  0.012352  0.082612           0.026885                   0.114305  0.279640            0.067083                  0.034325  0.025368  0.016764\n","44    0.016127  0.049108  0.073726              0.063857              0.047716  0.064144  0.026890  0.012303  0.083238           0.026784                   0.113884  0.278744            0.066989                  0.034243  0.025296  0.016951\n","45    0.016108  0.049015  0.073661              0.063677              0.047580  0.063989  0.027018  0.012350  0.083025           0.026711                   0.114311  0.278045            0.067845                  0.034503  0.025227  0.016936\n","46    0.016062  0.048950  0.073744              0.063727              0.047481  0.064066  0.027254  0.012340  0.083274           0.026696                   0.114840  0.277109            0.067880                  0.034451  0.025150  0.016976\n","47    0.016006  0.049212  0.074185              0.063862              0.047351  0.064067  0.027199  0.012300  0.083922           0.026613                   0.114453  0.276305            0.067969                  0.034383  0.025068  0.017106\n","\n","FEVD for Micro Finance News\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.005080  0.001121  0.003377              0.019414              0.004082  0.000198  0.005485  0.013348  0.004566           0.021602                   0.355809  0.052362            0.513556                  0.000000  0.000000  0.000000\n","1     0.005831  0.001119  0.020141              0.014312              0.039036  0.003027  0.005322  0.009785  0.020168           0.015821                   0.331576  0.092108            0.406379                  0.008942  0.014606  0.011827\n","2     0.005210  0.005044  0.017311              0.022739              0.043319  0.009901  0.004555  0.026000  0.032308           0.015642                   0.319090  0.078734            0.372465                  0.022817  0.014578  0.010288\n","3     0.005500  0.004508  0.015408              0.025098              0.040945  0.015647  0.018184  0.023190  0.041623           0.029304                   0.304062  0.072521            0.350341                  0.022331  0.016284  0.015056\n","4     0.008576  0.010228  0.028147              0.050882              0.039923  0.015097  0.019158  0.021261  0.055595           0.030870                   0.276222  0.066184            0.321773                  0.027510  0.014854  0.013723\n","5     0.008295  0.009601  0.033643              0.048544              0.037354  0.014624  0.024539  0.023346  0.054638           0.029012                   0.258890  0.064622            0.324528                  0.036321  0.014063  0.017980\n","6     0.008205  0.008616  0.030352              0.044925              0.035366  0.017212  0.036235  0.021174  0.049164           0.026403                   0.235784  0.058130            0.341631                  0.042020  0.028043  0.016741\n","7     0.015399  0.009446  0.028886              0.045938              0.033412  0.018304  0.034443  0.024239  0.062333           0.026343                   0.230898  0.054271            0.322505                  0.041521  0.034342  0.017721\n","8     0.015987  0.015758  0.032358              0.049276              0.035743  0.017200  0.036942  0.023640  0.072532           0.027740                   0.222932  0.056123            0.303957                  0.040594  0.032496  0.016721\n","9     0.015425  0.015247  0.032052              0.053636              0.038470  0.024009  0.040708  0.023359  0.069417           0.028170                   0.223271  0.054228            0.294644                  0.039891  0.031330  0.016143\n","10    0.014816  0.019696  0.031317              0.051766              0.039141  0.028977  0.042908  0.022557  0.081609           0.027491                   0.216553  0.052029            0.284326                  0.039312  0.030497  0.017003\n","11    0.014376  0.022843  0.039193              0.050658              0.040495  0.028110  0.041390  0.023301  0.087843           0.026491                   0.208632  0.050179            0.278686                  0.040858  0.030275  0.016670\n","12    0.013810  0.022572  0.039584              0.048970              0.038839  0.029153  0.041083  0.022172  0.083630           0.028797                   0.211565  0.048067            0.277312                  0.048474  0.028809  0.017163\n","13    0.013476  0.025795  0.038818              0.056485              0.038713  0.030655  0.040919  0.021468  0.084019           0.028398                   0.208289  0.047285            0.269567                  0.050774  0.027885  0.017455\n","14    0.013246  0.026830  0.037683              0.067836              0.037784  0.031925  0.039515  0.021194  0.086454           0.028061                   0.206122  0.046879            0.262549                  0.050121  0.026947  0.016857\n","15    0.013149  0.026013  0.036596              0.066524              0.036716  0.031088  0.040777  0.023964  0.083821           0.027206                   0.207974  0.046503            0.264514                  0.052392  0.026389  0.016373\n","16    0.013624  0.025973  0.037281              0.069418              0.036257  0.032956  0.040845  0.023989  0.087045           0.026529                   0.204849  0.046424            0.258970                  0.051398  0.026503  0.017938\n","17    0.013441  0.027147  0.041149              0.068510              0.035507  0.032437  0.040307  0.023474  0.088692           0.026919                   0.200345  0.046572            0.256789                  0.052400  0.027265  0.019044\n","18    0.013140  0.027146  0.041062              0.069511              0.034789  0.032120  0.040001  0.023248  0.086726           0.026333                   0.201075  0.045777            0.258489                  0.055244  0.026672  0.018668\n","19    0.012837  0.026678  0.040912              0.075159              0.034132  0.033156  0.039219  0.023001  0.091311           0.026160                   0.200706  0.044734            0.252884                  0.054549  0.026056  0.018508\n","20    0.012638  0.027822  0.042632              0.077283              0.033494  0.033412  0.038599  0.022670  0.094062           0.025764                   0.197995  0.044862            0.251021                  0.053881  0.025648  0.018218\n","21    0.013379  0.027421  0.042284              0.076233              0.033013  0.033185  0.039219  0.022823  0.092829           0.025510                   0.200346  0.044291            0.251522                  0.054136  0.025699  0.018109\n","22    0.013924  0.028563  0.044592              0.076000              0.032723  0.033328  0.040041  0.022877  0.094045           0.025234                   0.199108  0.044729            0.248079                  0.053370  0.025312  0.018071\n","23    0.013836  0.029381  0.049049              0.075744              0.032292  0.032837  0.039532  0.022552  0.096153           0.024905                   0.196046  0.044519            0.246277                  0.053107  0.024947  0.018823\n","24    0.013664  0.029467  0.048887              0.074988              0.031991  0.032694  0.039625  0.022387  0.095012           0.024816                   0.198346  0.044008            0.247184                  0.053643  0.024640  0.018649\n","25    0.013473  0.029508  0.049680              0.075801              0.031560  0.033365  0.040225  0.022332  0.098364           0.024630                   0.197851  0.043511            0.243700                  0.053109  0.024311  0.018579\n","26    0.013388  0.030658  0.051144              0.075549              0.031145  0.033715  0.039698  0.022036  0.100951           0.024496                   0.196550  0.043208            0.242035                  0.052686  0.023985  0.018754\n","27    0.013358  0.030360  0.050742              0.074830              0.030839  0.033484  0.040185  0.022801  0.099988           0.024626                   0.198292  0.042791            0.242113                  0.053212  0.023752  0.018626\n","28    0.013236  0.031255  0.051094              0.074804              0.030541  0.033762  0.041087  0.022654  0.101845           0.024559                   0.197516  0.042764            0.239798                  0.052813  0.023502  0.018771\n","29    0.013132  0.032336  0.052904              0.074446              0.030294  0.033550  0.040710  0.022642  0.102577           0.024329                   0.195765  0.042839            0.239144                  0.052662  0.023356  0.019313\n","30    0.013022  0.032293  0.052453              0.073789              0.030022  0.033428  0.041046  0.022548  0.101683           0.024498                   0.197363  0.042610            0.239739                  0.053227  0.023141  0.019139\n","31    0.012905  0.032441  0.053315              0.073902              0.029784  0.033887  0.041288  0.022457  0.102989           0.024519                   0.197258  0.042235            0.237567                  0.053005  0.022934  0.019514\n","32    0.012802  0.033255  0.054277              0.074091              0.029720  0.034093  0.040945  0.022380  0.103697           0.024340                   0.196248  0.042265            0.236722                  0.052717  0.022759  0.019690\n","33    0.012727  0.033026  0.053938              0.073522              0.029544  0.033994  0.041343  0.022685  0.102958           0.024212                   0.198087  0.041994            0.236679                  0.053121  0.022630  0.019538\n","34    0.012712  0.033494  0.054887              0.073626              0.029360  0.034610  0.041824  0.022600  0.103673           0.024024                   0.197353  0.041790            0.234952                  0.052936  0.022467  0.019692\n","35    0.012624  0.033999  0.056451              0.073880              0.029336  0.034522  0.041530  0.022448  0.104199           0.023854                   0.196166  0.041706            0.234027                  0.052760  0.022389  0.020109\n","36    0.012610  0.033839  0.056129              0.073456              0.029171  0.034464  0.041817  0.022433  0.103598           0.023813                   0.197276  0.041571            0.234396                  0.053163  0.022265  0.020000\n","37    0.012525  0.033977  0.056863              0.073742              0.029036  0.035016  0.042076  0.022447  0.104389           0.023658                   0.196876  0.041309            0.232850                  0.052954  0.022116  0.020165\n","38    0.012440  0.034547  0.057629              0.074128              0.029000  0.035151  0.041786  0.022292  0.105056           0.023518                   0.195981  0.041408            0.231936                  0.052758  0.021995  0.020375\n","39    0.012392  0.034389  0.057389              0.073759              0.028891  0.035084  0.042114  0.022424  0.104535           0.023406                   0.196912  0.041287            0.232093                  0.053152  0.021896  0.020277\n","40    0.012343  0.034572  0.057854              0.074047              0.028842  0.035523  0.042414  0.022392  0.105159           0.023277                   0.196351  0.041108            0.230964                  0.052932  0.021787  0.020436\n","41    0.012276  0.034950  0.058761              0.074271              0.028831  0.035500  0.042182  0.022290  0.105734           0.023148                   0.195397  0.041226            0.230233                  0.052864  0.021707  0.020629\n","42    0.012226  0.034851  0.058512              0.073933              0.028701  0.035404  0.042315  0.022279  0.105255           0.023088                   0.196396  0.041146            0.230628                  0.053116  0.021612  0.020536\n","43    0.012170  0.035015  0.058874              0.074101              0.028644  0.035719  0.042485  0.022262  0.105776           0.022993                   0.196293  0.040999            0.229563                  0.052920  0.021537  0.020648\n","44    0.012108  0.035469  0.059407              0.074290              0.028604  0.035838  0.042285  0.022149  0.106275           0.022881                   0.195577  0.041107            0.228971                  0.052810  0.021440  0.020790\n","45    0.012083  0.035347  0.059227              0.074006              0.028494  0.035774  0.042385  0.022216  0.105865           0.022802                   0.196391  0.041045            0.229222                  0.053060  0.021359  0.020725\n","46    0.012057  0.035543  0.059487              0.074261              0.028478  0.036136  0.042588  0.022170  0.106257           0.022720                   0.196058  0.040936            0.228338                  0.052888  0.021273  0.020811\n","47    0.012006  0.035875  0.060088              0.074378              0.028422  0.036143  0.042411  0.022080  0.106763           0.022634                   0.195338  0.041068            0.227824                  0.052788  0.021199  0.020982\n","\n","FEVD for International Trade News\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.011166  0.002829  0.003051              0.051125              0.000085  0.002730  0.008681  0.084807  0.005622           0.007614                   0.167193  0.017813            0.138077                  0.499205  0.000000  0.000000\n","1     0.045670  0.002045  0.008887              0.036838              0.028832  0.004164  0.011772  0.061272  0.025408           0.005475                   0.163586  0.030816            0.111293                  0.440989  0.006035  0.016918\n","2     0.041128  0.023200  0.012965              0.036112              0.025878  0.005558  0.013390  0.055021  0.048048           0.007772                   0.165343  0.030676            0.109869                  0.401791  0.005434  0.017814\n","3     0.043613  0.024489  0.012421              0.034619              0.027978  0.007556  0.013419  0.052844  0.059168           0.008600                   0.165554  0.032231            0.107293                  0.384812  0.006556  0.018849\n","4     0.042724  0.023648  0.014458              0.035574              0.027089  0.007255  0.013421  0.050802  0.071326           0.008221                   0.164855  0.032559            0.104848                  0.367598  0.016504  0.019118\n","5     0.043370  0.028269  0.013839              0.035093              0.033301  0.007192  0.012971  0.056281  0.075211           0.017009                   0.151737  0.034012            0.099014                  0.358391  0.015337  0.018972\n","6     0.040691  0.030386  0.013292              0.039301              0.031111  0.008274  0.023007  0.052742  0.070606           0.015957                   0.152963  0.039652            0.095836                  0.343647  0.024758  0.017778\n","7     0.038496  0.028686  0.017289              0.038425              0.036254  0.007832  0.026218  0.056890  0.075566           0.029176                   0.148205  0.038104            0.089809                  0.325082  0.026560  0.017410\n","8     0.036548  0.027483  0.024526              0.036937              0.035079  0.014992  0.026431  0.057577  0.085490           0.028164                   0.144651  0.036948            0.085351                  0.308922  0.033306  0.017595\n","9     0.039127  0.032458  0.023887              0.039516              0.036372  0.017482  0.029988  0.056024  0.083174           0.028327                   0.140850  0.035943            0.083065                  0.303807  0.032679  0.017301\n","10    0.037275  0.042831  0.027679              0.040489              0.034716  0.019024  0.030816  0.055664  0.091494           0.028448                   0.134351  0.034241            0.081418                  0.289886  0.031418  0.020250\n","11    0.036690  0.046785  0.028234              0.039715              0.035165  0.019923  0.030208  0.058970  0.091413           0.028034                   0.131812  0.033563            0.083218                  0.284353  0.031733  0.020185\n","12    0.036038  0.046000  0.028192              0.038948              0.035825  0.019735  0.029846  0.057742  0.090219           0.031382                   0.130724  0.036964            0.086351                  0.279248  0.033020  0.019766\n","13    0.036659  0.049154  0.028018              0.039069              0.035589  0.022162  0.029533  0.057316  0.089829           0.030906                   0.128988  0.036569            0.085438                  0.275876  0.032871  0.022025\n","14    0.036144  0.052055  0.027519              0.045738              0.038544  0.021635  0.029055  0.056518  0.087686           0.031057                   0.128927  0.037805            0.083365                  0.270141  0.032246  0.021565\n","15    0.035512  0.052104  0.027596              0.044870              0.037747  0.021412  0.028479  0.059711  0.085922           0.030430                   0.131905  0.039725            0.085202                  0.265620  0.032295  0.021470\n","16    0.036333  0.051357  0.027190              0.044945              0.037477  0.022464  0.028410  0.058863  0.086891           0.029995                   0.131583  0.039802            0.083988                  0.262131  0.034194  0.024378\n","17    0.035996  0.050880  0.026962              0.045258              0.037149  0.022741  0.029571  0.058245  0.086369           0.029803                   0.131055  0.040612            0.083876                  0.260650  0.036638  0.024194\n","18    0.035518  0.050203  0.026727              0.047928              0.036960  0.022487  0.030792  0.057690  0.085872           0.029439                   0.131622  0.040611            0.083883                  0.259863  0.036141  0.024261\n","19    0.035224  0.049723  0.027450              0.053018              0.036644  0.022495  0.031007  0.057061  0.087051           0.029102                   0.130408  0.040463            0.083100                  0.257350  0.035821  0.024083\n","20    0.035417  0.049434  0.027294              0.053782              0.036414  0.022402  0.031116  0.057064  0.088225           0.028964                   0.130371  0.040305            0.083633                  0.256047  0.035597  0.023934\n","21    0.036034  0.049638  0.027998              0.053230              0.036383  0.022486  0.033068  0.056800  0.087285           0.029379                   0.130385  0.039873            0.084748                  0.253401  0.035410  0.023881\n","22    0.036364  0.049656  0.030351              0.052819              0.036082  0.023272  0.033740  0.056356  0.087745           0.029566                   0.129418  0.040165            0.084042                  0.251407  0.035117  0.023902\n","23    0.036250  0.049658  0.033023              0.052485              0.035999  0.023125  0.033699  0.056183  0.087850           0.029389                   0.128912  0.039929            0.084327                  0.249869  0.035003  0.024298\n","24    0.036167  0.049633  0.033006              0.052700              0.035876  0.023760  0.033608  0.055990  0.087546           0.029320                   0.129327  0.039923            0.084828                  0.249044  0.035057  0.024215\n","25    0.035950  0.050224  0.033359              0.053521              0.035727  0.024057  0.033483  0.055714  0.088763           0.029170                   0.128575  0.040730            0.084293                  0.247376  0.034822  0.024236\n","26    0.035781  0.051020  0.033583              0.053394              0.035525  0.024211  0.033304  0.055395  0.089469           0.029461                   0.128541  0.040904            0.084616                  0.246028  0.034642  0.024128\n","27    0.035659  0.050779  0.033420              0.053172              0.035392  0.024104  0.033700  0.055187  0.089443           0.030000                   0.130033  0.040744            0.084899                  0.244929  0.034527  0.024013\n","28    0.035511  0.051278  0.033914              0.053394              0.035304  0.024164  0.033673  0.054927  0.090295           0.030206                   0.129610  0.041151            0.084499                  0.243777  0.034366  0.023932\n","29    0.035365  0.051643  0.033938              0.053184              0.035166  0.024059  0.033561  0.054961  0.090557           0.030114                   0.129518  0.041607            0.085016                  0.242947  0.034253  0.024111\n","30    0.035232  0.051414  0.033777              0.052985              0.035030  0.024234  0.034213  0.054869  0.090310           0.029991                   0.130394  0.041637            0.085687                  0.242101  0.034093  0.024034\n","31    0.035108  0.051189  0.034193              0.053492              0.034897  0.024309  0.034492  0.054628  0.091271           0.030058                   0.130116  0.041450            0.085301                  0.241078  0.033975  0.024441\n","32    0.035124  0.051151  0.034221              0.053497              0.034804  0.024272  0.034445  0.054584  0.091538           0.029977                   0.130237  0.041395            0.085809                  0.240542  0.033885  0.024518\n","33    0.035012  0.051153  0.034168              0.053308              0.034681  0.024455  0.034724  0.054760  0.091293           0.029872                   0.131116  0.041254            0.086057                  0.239904  0.033759  0.024484\n","34    0.034890  0.051627  0.034586              0.053379              0.034598  0.024873  0.034855  0.054596  0.091759           0.029764                   0.130735  0.041325            0.085748                  0.239050  0.033634  0.024580\n","35    0.034794  0.051759  0.035030              0.053346              0.034547  0.024840  0.034786  0.054479  0.091702           0.029703                   0.130796  0.041336            0.086187                  0.238563  0.033555  0.024576\n","36    0.034777  0.051622  0.034954              0.053234              0.034447  0.025216  0.034994  0.054335  0.091633           0.029643                   0.131089  0.041227            0.086718                  0.238145  0.033464  0.024504\n","37    0.034677  0.051759  0.035853              0.053504              0.034406  0.025425  0.034990  0.054176  0.091960           0.029557                   0.130707  0.041218            0.086428                  0.237393  0.033367  0.024581\n","38    0.034600  0.051849  0.036057              0.053593              0.034382  0.025449  0.034946  0.054049  0.092039           0.029497                   0.130785  0.041301            0.086711                  0.236886  0.033285  0.024573\n","39    0.034519  0.051727  0.035979              0.053477              0.034302  0.025508  0.035161  0.054052  0.091917           0.029450                   0.131482  0.041204            0.086947                  0.236505  0.033214  0.024556\n","40    0.034421  0.051773  0.036363              0.053806              0.034231  0.025655  0.035258  0.053966  0.092511           0.029371                   0.131157  0.041229            0.086679                  0.235790  0.033128  0.024662\n","41    0.034346  0.051809  0.036802              0.053860              0.034199  0.025605  0.035199  0.053858  0.092582           0.029307                   0.131170  0.041282            0.086811                  0.235394  0.033070  0.024708\n","42    0.034283  0.051712  0.036738              0.053817              0.034150  0.025769  0.035303  0.053826  0.092447           0.029290                   0.131531  0.041203            0.087158                  0.235100  0.033004  0.024669\n","43    0.034204  0.051783  0.036982              0.054077              0.034130  0.025876  0.035334  0.053718  0.092924           0.029244                   0.131325  0.041174            0.086960                  0.234561  0.032930  0.024776\n","44    0.034137  0.051824  0.037081              0.054150              0.034065  0.025863  0.035279  0.053631  0.093028           0.029189                   0.131395  0.041184            0.087312                  0.234221  0.032863  0.024778\n","45    0.034080  0.051736  0.037025              0.054068              0.034007  0.025914  0.035421  0.053589  0.092940           0.029147                   0.131889  0.041112            0.087555                  0.233975  0.032806  0.024736\n","46    0.034014  0.051882  0.037305              0.054214              0.033983  0.026090  0.035487  0.053512  0.093231           0.029090                   0.131680  0.041088            0.087375                  0.233497  0.032748  0.024805\n","47    0.033960  0.051926  0.037467              0.054185              0.033950  0.026069  0.035433  0.053470  0.093297           0.029043                   0.131685  0.041136            0.087572                  0.233273  0.032697  0.024837\n","\n","FEVD for EPU_US\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.010336  0.033232  0.002461              0.008925              0.005917  0.006924  0.000644  0.004642  0.014993           0.002576                   0.001673  0.003705            0.000586                  0.001417  0.901968  0.000000\n","1     0.008691  0.026860  0.013922              0.015139              0.008043  0.009868  0.005083  0.011516  0.020168           0.012052                   0.005952  0.007450            0.001088                  0.027874  0.816457  0.009837\n","2     0.010260  0.021176  0.011666              0.013273              0.018931  0.040169  0.014113  0.014173  0.015893           0.010294                   0.043442  0.013177            0.001525                  0.024516  0.721381  0.026010\n","3     0.012125  0.019985  0.023629              0.025816              0.018391  0.037016  0.012934  0.015196  0.018586           0.022939                   0.052549  0.011559            0.011318                  0.049748  0.631157  0.037053\n","4     0.016629  0.019313  0.032912              0.027138              0.022324  0.034577  0.013302  0.019483  0.017329           0.039347                   0.049616  0.015393            0.016815                  0.049613  0.587063  0.039144\n","5     0.050523  0.034011  0.032096              0.024413              0.029253  0.031018  0.012787  0.017572  0.022565           0.045826                   0.046563  0.023071            0.019086                  0.044553  0.528756  0.037906\n","6     0.046240  0.044331  0.029637              0.022531              0.031557  0.029262  0.013716  0.016085  0.043770           0.044228                   0.042683  0.041364            0.017540                  0.049821  0.488217  0.039019\n","7     0.045841  0.044146  0.028339              0.021564              0.031729  0.034156  0.013687  0.018605  0.050848           0.042333                   0.041518  0.045909            0.018498                  0.050031  0.468080  0.044716\n","8     0.047379  0.042982  0.027652              0.027112              0.031479  0.033263  0.013451  0.019289  0.054955           0.041329                   0.043235  0.050781            0.018188                  0.051048  0.454408  0.043448\n","9     0.046595  0.044437  0.028070              0.026907              0.031536  0.032904  0.017029  0.019503  0.063344           0.042062                   0.042228  0.050801            0.017680                  0.049956  0.443839  0.043108\n","10    0.045875  0.048408  0.031052              0.025873              0.031824  0.032702  0.018714  0.020944  0.061971           0.041072                   0.045126  0.049089            0.017513                  0.049180  0.434763  0.045894\n","11    0.044915  0.048316  0.032164              0.025530              0.031512  0.032857  0.018308  0.026107  0.060792           0.040701                   0.051060  0.047985            0.018422                  0.048102  0.428296  0.044933\n","12    0.044275  0.049649  0.031791              0.025184              0.031213  0.035433  0.018753  0.025882  0.060380           0.040189                   0.053603  0.048558            0.018168                  0.048324  0.422753  0.045844\n","13    0.043090  0.050165  0.031009              0.030351              0.032856  0.038938  0.018781  0.025395  0.059866           0.039673                   0.052328  0.051116            0.020787                  0.048817  0.411779  0.045048\n","14    0.043612  0.050078  0.031918              0.030502              0.032686  0.038607  0.018974  0.028955  0.059363           0.039352                   0.052413  0.050733            0.020661                  0.048412  0.408192  0.045541\n","15    0.043765  0.050080  0.031835              0.030402              0.032580  0.039184  0.019105  0.028944  0.059137           0.040300                   0.052373  0.050873            0.020580                  0.048378  0.407081  0.045381\n","16    0.044147  0.049704  0.031463              0.032325              0.032764  0.039154  0.020695  0.028767  0.059511           0.042889                   0.052622  0.051050            0.020324                  0.047776  0.401992  0.044819\n","17    0.045464  0.049036  0.031398              0.033406              0.032629  0.038826  0.024848  0.029422  0.058772           0.043143                   0.054165  0.050657            0.020565                  0.047407  0.395519  0.044743\n","18    0.045004  0.048526  0.032555              0.033258              0.032518  0.038703  0.024994  0.029810  0.061845           0.042685                   0.053794  0.050119            0.021115                  0.047613  0.391432  0.046027\n","19    0.045074  0.048717  0.032762              0.033323              0.032467  0.038646  0.025070  0.029766  0.062232           0.042609                   0.053697  0.050123            0.021084                  0.047546  0.390732  0.046152\n","20    0.045855  0.049161  0.032629              0.033249              0.032336  0.039298  0.025127  0.029639  0.062324           0.042432                   0.053756  0.050092            0.021283                  0.047366  0.389488  0.045965\n","21    0.045806  0.049375  0.033931              0.033634              0.032365  0.039655  0.024993  0.029554  0.062165           0.042705                   0.053411  0.049832            0.021276                  0.047981  0.387624  0.045695\n","22    0.045641  0.049246  0.034668              0.034583              0.032601  0.039512  0.024880  0.029529  0.061886           0.042987                   0.053631  0.049651            0.021552                  0.047811  0.385824  0.045997\n","23    0.045612  0.049372  0.034596              0.034550              0.032549  0.039454  0.025015  0.029468  0.061752           0.042999                   0.053886  0.049802            0.022297                  0.047790  0.384970  0.045889\n","24    0.045760  0.049656  0.034566              0.034923              0.032657  0.039517  0.024957  0.029390  0.061689           0.042873                   0.053792  0.050627            0.022309                  0.047660  0.383816  0.045808\n","25    0.046353  0.050340  0.034427              0.034918              0.032532  0.039542  0.024999  0.029262  0.061751           0.042687                   0.053841  0.051072            0.022798                  0.047469  0.382219  0.045790\n","26    0.046497  0.050665  0.034423              0.034824              0.032437  0.039529  0.024970  0.029176  0.061672           0.042564                   0.054466  0.050923            0.023446                  0.047347  0.381287  0.045772\n","27    0.046441  0.050615  0.034466              0.034929              0.032439  0.039846  0.024960  0.029147  0.061809           0.042599                   0.054402  0.050900            0.023538                  0.047309  0.380840  0.045760\n","28    0.046431  0.050531  0.034565              0.034953              0.032429  0.039779  0.024920  0.029149  0.062198           0.042699                   0.054483  0.050922            0.023717                  0.047312  0.380204  0.045709\n","29    0.046442  0.050458  0.034528              0.034977              0.032387  0.039757  0.024957  0.029254  0.062106           0.042663                   0.054586  0.050926            0.024154                  0.047509  0.379650  0.045646\n","30    0.046511  0.050427  0.034491              0.035040              0.032353  0.039740  0.024951  0.029229  0.062418           0.042641                   0.054757  0.050888            0.024135                  0.047523  0.379243  0.045653\n","31    0.046476  0.050414  0.034456              0.035219              0.032322  0.039699  0.024940  0.029221  0.062687           0.042717                   0.054699  0.050864            0.024354                  0.047480  0.378836  0.045617\n","32    0.046409  0.050344  0.034497              0.035164              0.032290  0.039804  0.025020  0.029233  0.062598           0.042654                   0.054868  0.050792            0.024893                  0.047641  0.378245  0.045550\n","33    0.046354  0.050301  0.034816              0.035226              0.032468  0.039921  0.025185  0.029171  0.062858           0.042674                   0.054837  0.050729            0.024917                  0.047685  0.377386  0.045472\n","34    0.046291  0.050391  0.035084              0.035230              0.032516  0.039885  0.025151  0.029179  0.062978           0.042678                   0.054840  0.050684            0.025192                  0.047620  0.376862  0.045418\n","35    0.046276  0.050321  0.035035              0.035234              0.032473  0.039842  0.025199  0.029222  0.062918           0.042619                   0.055077  0.050627            0.025751                  0.047694  0.376349  0.045363\n","36    0.046227  0.050414  0.035029              0.035320              0.032434  0.039931  0.025269  0.029228  0.063222           0.042566                   0.055143  0.050621            0.025725                  0.047675  0.375888  0.045308\n","37    0.046185  0.050446  0.035169              0.035427              0.032404  0.039928  0.025251  0.029209  0.063371           0.042531                   0.055126  0.050599            0.025875                  0.047632  0.375542  0.045307\n","38    0.046148  0.050398  0.035198              0.035404              0.032379  0.039975  0.025279  0.029191  0.063316           0.042563                   0.055348  0.050551            0.026107                  0.047660  0.375210  0.045274\n","39    0.046099  0.050423  0.035237              0.035495              0.032361  0.040072  0.025282  0.029166  0.063512           0.042645                   0.055358  0.050608            0.026079                  0.047615  0.374769  0.045280\n","40    0.046044  0.050472  0.035448              0.035549              0.032331  0.040029  0.025269  0.029134  0.063603           0.042618                   0.055369  0.050722            0.026244                  0.047597  0.374305  0.045268\n","41    0.045997  0.050440  0.035460              0.035512              0.032297  0.040006  0.025356  0.029116  0.063571           0.042601                   0.055661  0.050687            0.026509                  0.047648  0.373916  0.045222\n","42    0.045957  0.050423  0.035465              0.035593              0.032270  0.040071  0.025410  0.029093  0.063886           0.042590                   0.055684  0.050659            0.026491                  0.047622  0.373590  0.045195\n","43    0.045920  0.050433  0.035577              0.035680              0.032246  0.040043  0.025389  0.029073  0.064027           0.042555                   0.055728  0.050625            0.026607                  0.047625  0.373285  0.045189\n","44    0.045886  0.050397  0.035550              0.035653              0.032223  0.040049  0.025455  0.029091  0.063982           0.042540                   0.055923  0.050589            0.026769                  0.047735  0.373004  0.045155\n","45    0.045851  0.050396  0.035691              0.035694              0.032219  0.040085  0.025505  0.029098  0.064065           0.042517                   0.055982  0.050564            0.026749                  0.047709  0.372717  0.045158\n","46    0.045831  0.050400  0.035813              0.035693              0.032200  0.040064  0.025488  0.029077  0.064109           0.042494                   0.055967  0.050549            0.026928                  0.047729  0.372472  0.045186\n","47    0.045810  0.050370  0.035793              0.035689              0.032182  0.040056  0.025497  0.029075  0.064082           0.042492                   0.056167  0.050519            0.027077                  0.047779  0.372248  0.045163\n","\n","FEVD for EPU_UK\n","        GBPUSD    CPI_US    CPI_UK  Money Market Rate_US  Money Market Rate_UK    IPI_US    IPI_UK     M2_US     M2_UK  Stock Market News  Economic Development News  FED News  Micro Finance News  International Trade News    EPU_US    EPU_UK\n","0     0.004516  0.000010  0.007869              0.016955              0.044257  0.001084  0.000026  0.000691  0.029768           0.043574                   0.001273  0.000014            0.004060                  0.001751  0.008983  0.835167\n","1     0.003821  0.004984  0.042726              0.014364              0.036290  0.002626  0.014807  0.005406  0.026089           0.035617                   0.012740  0.005551            0.034720                  0.001881  0.007992  0.750387\n","2     0.011380  0.006064  0.045370              0.013039              0.039722  0.023998  0.016864  0.006456  0.023471           0.038387                   0.015907  0.005334            0.032231                  0.011411  0.026039  0.684326\n","3     0.023327  0.006678  0.043388              0.013048              0.038757  0.023170  0.023021  0.011214  0.031642           0.040248                   0.017487  0.005019            0.030326                  0.023903  0.024923  0.643849\n","4     0.031768  0.006316  0.050588              0.029298              0.037012  0.025327  0.022160  0.010765  0.031639           0.037726                   0.035094  0.004927            0.030412                  0.022370  0.024814  0.599782\n","5     0.030542  0.009983  0.048179              0.038625              0.034834  0.036279  0.023092  0.011448  0.029747           0.034763                   0.032256  0.020629            0.045863                  0.020719  0.031225  0.551814\n","6     0.034016  0.017157  0.048927              0.037627              0.032964  0.039456  0.022380  0.012311  0.041533           0.034088                   0.030946  0.019702            0.046604                  0.020234  0.039896  0.522159\n","7     0.032723  0.025300  0.048922              0.035707              0.035067  0.041599  0.023012  0.025326  0.039705           0.033194                   0.037708  0.018955            0.044273                  0.023563  0.039304  0.495639\n","8     0.032270  0.026550  0.048974              0.039512              0.034330  0.040676  0.023047  0.027142  0.048625           0.033052                   0.042052  0.018506            0.044457                  0.022998  0.038047  0.479762\n","9     0.033306  0.033682  0.048250              0.039879              0.033541  0.040633  0.023627  0.027539  0.051048           0.032277                   0.043861  0.019707            0.043577                  0.023170  0.037122  0.468783\n","10    0.037888  0.033183  0.049072              0.039057              0.032817  0.040951  0.023213  0.028445  0.053791           0.033212                   0.042825  0.020552            0.043244                  0.025897  0.037435  0.458417\n","11    0.040733  0.033100  0.047921              0.039903              0.032789  0.051476  0.022734  0.030499  0.052525           0.032482                   0.041925  0.020070            0.042212                  0.025310  0.036717  0.449603\n","12    0.040530  0.032550  0.047158              0.039553              0.033052  0.051870  0.022800  0.030717  0.053936           0.036043                   0.041299  0.019782            0.041563                  0.024948  0.041022  0.443177\n","13    0.040428  0.033368  0.047426              0.039656              0.032894  0.052570  0.024074  0.032640  0.054992           0.035944                   0.041166  0.019757            0.041016                  0.024625  0.041979  0.437466\n","14    0.040836  0.032911  0.047967              0.039122              0.032474  0.051869  0.024047  0.035239  0.056372           0.036191                   0.040607  0.020309            0.040695                  0.024342  0.043744  0.433276\n","15    0.040905  0.032784  0.047776              0.039912              0.032332  0.051636  0.025279  0.035270  0.056898           0.036568                   0.040365  0.021371            0.040524                  0.024201  0.043486  0.430693\n","16    0.040586  0.032733  0.047828              0.040205              0.032101  0.053111  0.025340  0.035436  0.058531           0.036593                   0.040737  0.021204            0.040397                  0.024027  0.043287  0.427884\n","17    0.040613  0.033386  0.047663              0.040442              0.031998  0.052971  0.025728  0.035445  0.058316           0.036560                   0.040814  0.021144            0.040372                  0.024501  0.043208  0.426839\n","18    0.041742  0.033851  0.047445              0.040752              0.031882  0.052687  0.025755  0.035382  0.058620           0.036536                   0.041183  0.021109            0.040407                  0.025186  0.043101  0.424362\n","19    0.041595  0.034326  0.047239              0.041410              0.031974  0.052837  0.025686  0.035434  0.058640           0.036405                   0.042068  0.021647            0.040634                  0.025054  0.042878  0.422170\n","20    0.041945  0.034419  0.047307              0.042045              0.031897  0.052702  0.025690  0.035482  0.058588           0.036446                   0.042292  0.021629            0.040990                  0.025204  0.042700  0.420663\n","21    0.041947  0.034581  0.047514              0.041937              0.031911  0.052547  0.025890  0.035795  0.058601           0.036301                   0.043566  0.021646            0.040881                  0.025105  0.042711  0.419066\n","22    0.041932  0.034830  0.047692              0.042040              0.031847  0.052472  0.025840  0.035789  0.058473           0.036235                   0.043503  0.021603            0.041391                  0.025584  0.042624  0.418147\n","23    0.042028  0.035135  0.047822              0.041946              0.031783  0.052395  0.026159  0.035737  0.058682           0.036160                   0.043453  0.021724            0.041412                  0.025711  0.042577  0.417276\n","24    0.042448  0.035191  0.047947              0.041917              0.031746  0.052362  0.026193  0.035674  0.058841           0.036231                   0.043445  0.021842            0.041339                  0.025710  0.042553  0.416563\n","25    0.042533  0.035148  0.047854              0.041872              0.031774  0.052461  0.026174  0.035831  0.058710           0.036840                   0.043382  0.021792            0.041410                  0.025817  0.042601  0.415800\n","26    0.042464  0.035439  0.048359              0.041818              0.031749  0.052367  0.026127  0.035812  0.058677           0.037049                   0.043305  0.021866            0.041583                  0.025773  0.042551  0.415060\n","27    0.042409  0.035826  0.048318              0.041832              0.031854  0.052324  0.026095  0.035779  0.058609           0.037013                   0.043351  0.021978            0.041758                  0.025767  0.042503  0.414585\n","28    0.042501  0.035895  0.048300              0.041795              0.031892  0.052279  0.026077  0.035899  0.058702           0.036981                   0.043336  0.021980            0.041830                  0.025818  0.042491  0.414222\n","29    0.042483  0.035872  0.048273              0.042002              0.031900  0.052294  0.026063  0.035877  0.058773           0.036979                   0.043349  0.021974            0.041805                  0.025805  0.042588  0.413962\n","30    0.042459  0.035929  0.048545              0.041975              0.031899  0.052254  0.026085  0.035859  0.058733           0.036950                   0.043338  0.021962            0.041812                  0.025941  0.042606  0.413654\n","31    0.042429  0.035905  0.048520              0.042018              0.031949  0.052216  0.026068  0.035839  0.058691           0.037070                   0.043543  0.021947            0.041827                  0.025969  0.042655  0.413354\n","32    0.042410  0.035892  0.048553              0.042001              0.031940  0.052200  0.026097  0.035892  0.058661           0.037062                   0.043694  0.021934            0.041894                  0.026025  0.042631  0.413115\n","33    0.042416  0.035904  0.048536              0.041981              0.031954  0.052175  0.026080  0.035883  0.058637           0.037093                   0.043839  0.021983            0.041955                  0.026035  0.042619  0.412910\n","34    0.042434  0.035898  0.048549              0.041967              0.031940  0.052179  0.026089  0.035868  0.058673           0.037098                   0.043882  0.021995            0.041940                  0.026032  0.042664  0.412792\n","35    0.042423  0.035889  0.048618              0.042046              0.031925  0.052177  0.026115  0.035855  0.058688           0.037081                   0.043922  0.022002            0.041921                  0.026023  0.042648  0.412668\n","36    0.042418  0.035881  0.048697              0.042047              0.031916  0.052161  0.026107  0.035852  0.058746           0.037080                   0.043910  0.022005            0.041912                  0.026038  0.042635  0.412596\n","37    0.042410  0.035873  0.048701              0.042035              0.031911  0.052144  0.026135  0.035856  0.058728           0.037068                   0.043986  0.021998            0.041941                  0.026033  0.042644  0.412538\n","38    0.042392  0.035859  0.048867              0.042019              0.031906  0.052138  0.026155  0.035845  0.058775           0.037066                   0.044015  0.021995            0.041932                  0.026028  0.042638  0.412368\n","39    0.042377  0.035910  0.048925              0.042019              0.031893  0.052148  0.026175  0.035838  0.058751           0.037126                   0.044097  0.021984            0.041953                  0.026023  0.042617  0.412163\n","40    0.042371  0.035935  0.048920              0.042020              0.031920  0.052147  0.026177  0.035837  0.058763           0.037120                   0.044097  0.021990            0.041948                  0.026021  0.042611  0.412123\n","41    0.042369  0.035978  0.048928              0.042052              0.031921  0.052147  0.026175  0.035833  0.058759           0.037117                   0.044096  0.021989            0.041942                  0.026018  0.042605  0.412070\n","42    0.042370  0.035992  0.048928              0.042052              0.031920  0.052147  0.026176  0.035831  0.058753           0.037112                   0.044093  0.021992            0.041984                  0.026021  0.042601  0.412030\n","43    0.042362  0.035997  0.048936              0.042044              0.031914  0.052138  0.026181  0.035833  0.058740           0.037118                   0.044186  0.021995            0.041996                  0.026016  0.042599  0.411944\n","44    0.042355  0.036002  0.048970              0.042037              0.031910  0.052130  0.026179  0.035831  0.058759           0.037127                   0.044221  0.021991            0.041996                  0.026011  0.042592  0.411886\n","45    0.042346  0.036039  0.048966              0.042030              0.031915  0.052133  0.026173  0.035828  0.058775           0.037118                   0.044290  0.022013            0.041989                  0.026007  0.042586  0.411791\n","46    0.042339  0.036032  0.048961              0.042022              0.031912  0.052141  0.026184  0.035854  0.058774           0.037118                   0.044335  0.022033            0.041988                  0.026016  0.042580  0.411711\n","47    0.042333  0.036032  0.048974              0.042031              0.031910  0.052142  0.026204  0.035850  0.058801           0.037115                   0.044329  0.022030            0.041991                  0.026018  0.042580  0.411662\n","\n","\n"]}],"source":["# autocorrelation with itself dominates\n","fevd = results.fevd(48)\n","#fevd.summary()\n","fevd_summary = fevd.summary()\n","fevd_summary\n"]},{"cell_type":"code","source":["fevd.plot()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"LRr60homRLWe","executionInfo":{"status":"ok","timestamp":1711360767466,"user_tz":-60,"elapsed":33869,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"50447697-e1ec-4f44-f865-7929f4c8b9ec"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Figure size 1000x1000 with 16 Axes>"],"image/png":"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\n"},"metadata":{},"execution_count":54},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x1000 with 16 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c9ksV2wpInU4"},"outputs":[],"source":["#print latex(fevd_summary)"]},{"cell_type":"code","source":["#colors = [\"#00338D\", \"#005EB8\", \"#0091DA\", \"#483698\", \"#470A68\", \"#009A44\", \"#43B02A\", \"#765341\", \"#C6007E\", \"#6D2077\", \"#00A3A1\", \"#ff9933\", \"#BC204B\", \"#333333\", \"#EAAA00\", \"#F68D2E\"]\n"],"metadata":{"id":"wWjyRw-RC8go"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Sorry, I could not plot this here, I plot it in r. This plot is wrong and as I already have it in r, I do not do the double work here.\n","\n","\n"],"metadata":{"id":"CE4QMPZFoX7w"}},{"cell_type":"code","source":["#This plot is wrong.\n","import matplotlib.pyplot as plt\n","\n","# Assuming you have already computed and obtained 'fevd_var' from the VAR model\n","\n","# Extract FEVD values\n","fevd_values = fevd_var.decomp\n","\n","# Get the names of variables\n","variable_names = data.columns\n","\n","# Plot FEVD as time series\n","fig, ax = plt.subplots(figsize=(10, 6))\n","for i in range(1): #len(variable_names)\n","    ax.plot(fevd_values[:, i, i], label=variable_names[i])\n","\n","ax.set_title('FEVD as Time Series')\n","ax.set_xlabel('Horizon')\n","ax.set_ylabel('FEVD')\n","ax.legend()\n","plt.grid(True)\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":225},"id":"LLiYViXZJb8m","executionInfo":{"status":"error","timestamp":1711360767467,"user_tz":-60,"elapsed":13,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"65889f16-b29d-482f-9a09-bef969ead427"},"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'fevd_var' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-57-5668aea1517b>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Extract FEVD values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mfevd_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfevd_var\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecomp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Get the names of variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'fevd_var' is not defined"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DmqXJP7m8nyk"},"outputs":[],"source":["# Display the DataFrame in LaTeX format\n","#print(data_summary.to_latex())\n","\n","# Export the DataFrame to a LaTeX file\n","#df_summary.to_latex(\"summary.tex\")"]},{"cell_type":"markdown","source":["# Noncumulative irfs of GBP/USD"],"metadata":{"id":"Zk3lGqKjWSVL"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"LAoUvCNLUW6o"},"outputs":[],"source":["print(irf.irfs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BTRdquA5ikiu"},"outputs":[],"source":["results.fevd(48).plot(figsize=(30,30));\n","print(\"FEVD\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"16-EDIlbFBtR"},"outputs":[],"source":["import statsmodels.api as sm\n","results_summary = results.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AKwRXCz1ttcV"},"outputs":[],"source":["fevd"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jxIarrFHnpL1"},"outputs":[],"source":["#!pip install rpy2==3.5.1\n","#%load_ext rpy2.ipython"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sq_1vdgJrNfk"},"outputs":[],"source":["#%%R\n","\n","#plot(results.fevd(20), col=2:15)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TcEitqPk9e9B"},"outputs":[],"source":["#gc = results.test_causality('x', ['x', 'y'], kind='f')\n","#gc.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NiwnPK7r9lxu"},"outputs":[],"source":["# forecast\n","results.plot_forecast(10);\n"]},{"cell_type":"code","source":[],"metadata":{"id":"2NBIki0ynTUM"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Noncumulative irfs"],"metadata":{"id":"1mKhSLjvnTgT"}},{"cell_type":"markdown","source":["Please note that we only present the cummulative irfs in the appendix, which r allows to be calculated. These irfs here are not cummulative and are not presented in my study."],"metadata":{"id":"yC3QVmqcnTgU"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"C60DNhSInTgU"},"outputs":[],"source":["irf = results.irf(48)\n","\n","#print(irf.irfs)\n","fig=irf.plot();\n","fig.set_size_inches(23.5, 15.5)\n","print(\"IRF\")"]},{"cell_type":"code","source":["from pickle import TRUE\n","irf.plot(response='GBPUSD', impulse = 'Stock Market News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"],"metadata":{"id":"mNFJ7ftRnTgU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["irf.plot(response='GBPUSD', impulse = 'Economic Development News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"],"metadata":{"id":"yPbbXy4nnTgV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#irf.plot(response='GBPUSD', impulse = 'FED News')\n","irf.plot(response='GBPUSD', impulse = 'FED News', orth=TRUE, signif=0.05);\n","\n","print(\"IRF\")"],"metadata":{"id":"p9LBctp1nTgV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["irf.plot(response='GBPUSD', impulse = 'Micro Finance News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"],"metadata":{"id":"hvdGGKiynTgV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["irf.plot(response='GBPUSD', impulse = 'International Trade News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"],"metadata":{"id":"h1N9hXLknTgV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"VDLf986Nn1-2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Please note that we only present the cummulative irfs in the appendix, which r allows to be calculated. These irfs here are not cummulative and are not presented in my study."],"metadata":{"id":"J8DzW9T9m3R9"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"Uvu3DPcl9Ukj"},"outputs":[],"source":["irf = results.irf(48)\n","\n","#print(irf.irfs)\n","fig=irf.plot();\n","fig.set_size_inches(23.5, 15.5)\n","print(\"IRF\")"]},{"cell_type":"code","source":["from pickle import TRUE\n","irf.plot(response='GBPUSD', impulse = 'Stock Market News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"],"metadata":{"id":"LgPMQ29UNxiV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["irf.plot(response='GBPUSD', impulse = 'Economic Development News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"],"metadata":{"id":"XU4bHcBAN1Rk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#irf.plot(response='GBPUSD', impulse = 'FED News')\n","irf.plot(response='GBPUSD', impulse = 'FED News', orth=TRUE, signif=0.05);\n","\n","print(\"IRF\")"],"metadata":{"id":"UNjxlcXwODha"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["irf.plot(response='GBPUSD', impulse = 'Micro Finance News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"],"metadata":{"id":"WyogUINsO5GH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["irf.plot(response='GBPUSD', impulse = 'International Trade News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"],"metadata":{"id":"raav-IzbOy4w"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XVVFTbhggfrQ"},"outputs":[],"source":["import matplotlib.pyplot as plt\n","plt.figure(figsize=(185,65))\n","#fig.set_size_inches(135,75)\n","irf.plot(response='GBPUSD')\n","print('irf')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8oaUS_Z1hW7P"},"outputs":[],"source":["from pickle import TRUE\n","irf.plot(response='GBPUSD', impulse = 'Stock Market News', orth=TRUE, signif=0.1)\n","print(\"IRF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iCYYNsYHlHA4"},"outputs":[],"source":["irf.plot(response='GBPUSD', impulse = 'Economic Development News', orth=TRUE, signif=0.1)\n","print(\"IRF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yfDffz5WlHq9"},"outputs":[],"source":["#irf.plot(response='GBPUSD', impulse = 'FED News')\n","irf.plot(response='GBPUSD', impulse = 'FED News', orth=TRUE, signif=0.1);\n","\n","print(\"IRF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zr7U85uglINi"},"outputs":[],"source":["from pickle import TRUE\n","#irf.plot(response='GBPUSD', impulse = 'Micro Finance News')\n","irf.plot(response='GBPUSD', impulse = 'Micro Finance News', orth=TRUE, signif=0.1);\n","print(\"IRF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4uTS3o-hlI74"},"outputs":[],"source":["irf.plot(response='GBPUSD', impulse = 'International Trade News', orth=TRUE, signif=0.05)\n","print(\"IRF\")"]},{"cell_type":"code","source":["import numpy as np\n","\n","# Assuming 'irf' is your impulse response functions obtained from your model\n","\n","# Get the number of time periods and the number of variables\n","n_periods =10\n","n_variables =16\n","\n","# Initialize an array to store cumulative impulse response functions\n","cirf = np.zeros_like(irf)\n","\n","# Calculate cumulative impulse response functions\n","for t in range(1, n_periods):\n","    cirf[t] = cirf[t-1] + irf[t]\n","\n","# Plot cumulative impulse response functions\n","\n","variable_index = 0  # Assuming you want to plot the first variable\n","plt.plot(cirf[:, variable_index, :])\n","plt.title('Cumulative Impulse Response Functions')\n","plt.xlabel('Time')\n","plt.ylabel('Cumulative Response')\n","plt.show()\n"],"metadata":{"id":"vOq6nDYbRj0P"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","\n","# Assuming 'irf' is your impulse response functions obtained from your model\n","\n","# Get the number of time periods and the number of variables\n","n_periods =10\n","n_variables =16\n","\n","# Initialize an array to store cumulative impulse response functions\n","cirf = np.zeros_like(irf)\n","\n","# Calculate cumulative impulse response functions\n","for t in range(1, n_periods):\n","    cirf[t] = cirf[t-1] + irf[t]\n","\n","# Plot cumulative impulse response functions\n","\n","variable_index = 0  # Assuming you want to plot the first variable\n","plt.plot(cirf[:, variable_index, :])\n","plt.title('Cumulative Impulse Response Functions')\n","plt.xlabel('Time')\n","plt.ylabel('Cumulative Response')\n","plt.show()\n"],"metadata":{"id":"4PyrTOnMnTgV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["fevd_var.plot(0)\n"],"metadata":{"id":"ZRpkKvelnTgV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["colors = [\"#00338D\", \"#005EB8\", \"#0091DA\", \"#483698\", \"#470A68\", \"#009A44\", \"#43B02A\", \"#765341\", \"#C6007E\", \"#6D2077\", \"#00A3A1\", \"#ff9933\", \"#BC204B\", \"#333333\", \"#EAAA00\", \"#F68D2E\"]\n","fevd_results.plot(0)\n"],"metadata":{"id":"McXua0vrnTgW"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZUy16U4QnTgW"},"outputs":[],"source":["# Display the DataFrame in LaTeX format\n","#print(data_summary.to_latex())\n","\n","# Export the DataFrame to a LaTeX file\n","#df_summary.to_latex(\"summary.tex\")"]},{"cell_type":"markdown","source":["# Cummulative IRFs"],"metadata":{"id":"VTIWXuz-p4Q0"}},{"cell_type":"code","source":["# PLEASE NOTE THAT THERE CAN BE A BUG HERE, AS IT WAS REPORTED IN THE INTERNET. I CALCULATE AND PLOT CUMMULATIVE IRFs in R"],"metadata":{"id":"b0B04MwvqkjY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["irf.cum_effects[1]"],"metadata":{"id":"aq7RW0FgXgip"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["m,n,r = irf.cum_effects.shape\n","out_arr = np.column_stack((np.repeat(np.arange(m),n),irf.cum_effects.reshape(m*n,-1)))\n","cirf_df = pd.DataFrame(out_arr)\n","cirf_df"],"metadata":{"id":"LqsddWPyHGT0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["x=irf.cum_effect_stderr(48)\n","m,n,r = irf.cum_effect_stderr(48).shape\n","out_arr = np.column_stack((np.repeat(np.arange(m),n),x.reshape(m*n,-1)))\n","cirf_stderr_df = pd.DataFrame(out_arr)\n","cirf_stderr_df"],"metadata":{"id":"AfWhM69j4Bul"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# cummulative irfs as numbers\n","print(irf.cum_effects)\n"],"metadata":{"id":"Lw8v4JwZ3z-J"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ia9J1leb1u2B"},"outputs":[],"source":["#from pickle import TRUE\n","from google.colab import files\n","import matplotlib.ticker as mtick\n","#getAnywhere(irf.plot_cum_effects)\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='Stock Market News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Stock Market News.pdf\", bbox_inches='tight', pad_inches=0.001) #in order not to get the borders when downloading.\n","files.download(\"Cumulative Impulse Response function of GBPUSD to Stock Market News.pdf\")\n","\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='Economic Development News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Economic Development News.pdf\", bbox_inches='tight', pad_inches=0.001)\n","files.download(\"Cumulative Impulse Response function of GBPUSD to Economic Development News.pdf\")\n","\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='FED News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Cumulative Impulse Response function of GBPUSD to FED News.pdf\", bbox_inches='tight', pad_inches=0.001)\n","files.download(\"Cumulative Impulse Response function of GBPUSD to FED News.pdf\")\n","\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='Micro Finance News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Micro Finance News.pdf\", bbox_inches='tight', pad_inches=0.001)\n","files.download(\"Cumulative Impulse Response function of GBPUSD to Micro Finance News.pdf\")\n","\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='International Trade News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Cumulative Impulse Response function of GBPUSD to International Trade.pdf\", bbox_inches='tight', pad_inches=0.001)\n","files.download(\"Cumulative Impulse Response function of GBPUSD to International Trade.pdf\")"]},{"cell_type":"code","source":["#from pickle import TRUE\n","from google.colab import files\n","import matplotlib.ticker as mtick\n","#getAnywhere(irf.plot_cum_effects)\n","plt=irf.plot(response='GBPUSD', impulse='Stock Market News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Impulse Response function of GBPUSD to Stock Market News.pdf\", bbox_inches='tight', pad_inches=0.001) #in order not to get the borders when downloading.\n","files.download(\"Impulse Response function of GBPUSD to Stock Market News.pdf\")\n","\n","plt=irf.plot(response='GBPUSD', impulse='Economic Development News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Impulse Response function of GBPUSD to Economic Development News.pdf\", bbox_inches='tight', pad_inches=0.001)\n","files.download(\"Impulse Response function of GBPUSD to Economic Development News.pdf\")\n","\n","plt=irf.plot(response='GBPUSD', impulse='FED News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Impulse Response function of GBPUSD to FED News.pdf\", bbox_inches='tight', pad_inches=0.001)\n","files.download(\"Impulse Response function of GBPUSD to FED News.pdf\")\n","\n","plt=irf.plot(response='GBPUSD', impulse='Micro Finance News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Impulse Response function of GBPUSD to Micro Finance News.pdf\", bbox_inches='tight', pad_inches=0.001)\n","files.download(\"Impulse Response function of GBPUSD to Micro Finance News.pdf\")\n","\n","plt=irf.plot(response='GBPUSD', impulse='International Trade News', orth=TRUE, signif=0.05)\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","plt.savefig(\"Impulse Response function of GBPUSD to International Trade.pdf\", bbox_inches='tight', pad_inches=0.001)\n","files.download(\"Impulse Response function of GBPUSD to International Trade.pdf\")"],"metadata":{"id":"DZfdDIT-ZPyR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#from pickle import TRUE\n","from google.colab import files\n","import matplotlib.ticker as mtick\n","#getAnywhere(irf.plot_cum_effects)\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='GBPUSD', orth=TRUE, signif=0.1) #plot_stderr= False\n","\n","#plt=irf.plot_cum_effects(response='GBPUSD', impulse='Stock Market News', orth=TRUE, signif=0.1) #plot_stderr= False\n","plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","# plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Stock Market News_0.1.pdf\", bbox_inches='tight', pad_inches=0.001) #in order not to get the borders when downloading.\n","# files.download(\"Cumulative Impulse Response function of GBPUSD to Stock Market News_0.1.pdf\")\n","\n","# plt=irf.plot_cum_effects(response='GBPUSD', impulse='Economic Development News', orth=TRUE, signif=0.1)\n","# plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","# plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Economic Development News_0.1.pdf\", bbox_inches='tight', pad_inches=0.001)\n","# files.download(\"Cumulative Impulse Response function of GBPUSD to Economic Development News_0.1.pdf\")\n","\n","# plt=irf.plot_cum_effects(response='GBPUSD', impulse='FED News', orth=TRUE, signif=0.1)\n","# plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","# plt.savefig(\"Cumulative Impulse Response function of GBPUSD to FED News_0.1.pdf\", bbox_inches='tight', pad_inches=0.001)\n","# files.download(\"Cumulative Impulse Response function of GBPUSD to FED News_0.1.pdf\")\n","\n","# plt=irf.plot_cum_effects(response='GBPUSD', impulse='Micro Finance News', orth=TRUE, signif=0.1)\n","# plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","# plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Micro Finance News_0.1.pdf\", bbox_inches='tight', pad_inches=0.001)\n","# files.download(\"Cumulative Impulse Response function of GBPUSD to Micro Finance News_0.1.pdf\")\n","\n","# plt=irf.plot_cum_effects(response='GBPUSD', impulse='International Trade News', orth=TRUE, signif=0.1)\n","# plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))\n","# plt.savefig(\"Cumulative Impulse Response function of GBPUSD to International Trade_0.1.pdf\", bbox_inches='tight', pad_inches=0.001)\n","# files.download(\"Cumulative Impulse Response function of GBPUSD to International Trade_0.1.pdf\")"],"metadata":{"id":"G8QPCxpG_0Ni"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#from pickle import TRUE\n","import matplotlib.ticker as mtick\n","from google.colab import files\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='Stock Market News', orth=TRUE, signif=0.1)\n","plt"],"metadata":{"id":"RZkY2EMC57Mq"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zdBBAhLvmegS"},"outputs":[],"source":["#from pickle import TRUE\n","from google.colab import files\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='Stock Market News', orth=TRUE, signif=0.2)\n","plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Stock Market News.pdf\")\n","files.download(\"Cumulative Impulse Response function of GBPUSD to Stock Market News.pdf\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ulemEUF6n818"},"outputs":[],"source":["\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='Economic Development News', orth=TRUE, signif=0.2)\n","plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Economic Development News.pdf\")\n","files.download(\"Cumulative Impulse Response function of GBPUSD to Economic Development News.pdf\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kS2udbzq9XSn"},"outputs":[],"source":["fig =irf.plot_cum_effects(orth=TRUE);\n","fig.set_size_inches(23.5, 15.5)\n","fig\n","#This is matrix of 1.USDEUR\tCPI_US\t2.CPI_EU\t3.Money_Market_Rate_US\t4.Money_Market_Rate_EU\t5.IPI_US\t6.IPI_EU\t7.M2_US\t8.M2_EU\tStock Market News\tEconomic Development News\tFED News\tMicro Finance News\tInternational Trade News\n","print(\"All CIRFs\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FHDrQ48H7SKV"},"outputs":[],"source":["from pickle import FALSE\n","fig=irf.plot_cum_effects(response='GBPUSD', impulse='Stock Market News', orth=TRUE)\n","print()\n","fig.plot()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"I4FsXdlDCufw"},"outputs":[],"source":["irf.plot_cum_effects(response='GBPUSD', impulse='Stock Market News', orth=TRUE, signif=0.1)\n","print(\"CIRF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2a-Nh0MtWukt"},"outputs":[],"source":["from google.colab import files\n","plt=irf.plot_cum_effects(response='GBPUSD', impulse='Stock Market News', orth=TRUE, signif=0.1)\n","plt.savefig(\"Cumulative Impulse Response function of GBPUSD to Stock Market News.pdf\")\n","files.download(\"Cumulative Impulse Response function of GBPUSD to Stock Market News.pdf\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"71sRScQCoiHU"},"outputs":[],"source":["irf.plot_cum_effects(response='GBPUSD', impulse='Economic Development News', orth=TRUE, signif=0.1)\n","print(\"CIRF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rCXj17evoiXy"},"outputs":[],"source":["irf.plot_cum_effects(response='GBPUSD', impulse='FED News', orth=TRUE, signif=0.1)\n","print(\"CIRF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BXC1DW5joiiv"},"outputs":[],"source":["irf.plot_cum_effects(response='GBPUSD', impulse='Micro Finance News', orth=TRUE, signif=0.1)\n","print(\"CIRF\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KzLTWqHcoixq"},"outputs":[],"source":["irf.plot_cum_effects(response='GBPUSD', impulse='International Trade News', orth=TRUE, signif=0.1)\n","print(\"CIRF\")"]},{"cell_type":"code","source":["##cirfs = pd.DataFrame(irf.cum_effects[:, 1])\n","##cirfs"],"metadata":{"id":"1eotmMPIsD_h"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#Grangercausality tests"],"metadata":{"id":"bUueHORMdWuc"}},{"cell_type":"code","source":["# Granger causality of each signle lag\n","\n","print(\"Topic 1\")\n","data['allnews'] = data['Stock Market News'] + data['Economic Development News'] + data['FED News'] +data['Micro Finance News'] +data['International Trade News']\n","for i in range (1,8):\n","  gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Stock Market News\"]], [i])\n","  print(i)\n","  print(gc_res)\n","\n","print(\"Topic 2\")\n","for i in range (1,8):\n","  gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Economic Development News\"]], [i])\n","  print(i)\n","  print(gc_res)\n","\n","print(\"Topic 3\")\n","for i in range (1,8):\n","  gc_res = grangercausalitytests(data[[\"GBPUSD\", \"FED News\"]], [i])\n","  print(i)\n","  print(gc_res)\n","print(\"Topic 4\")\n","for i in range (1,8):\n","  gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Micro Finance News\"]], [i])\n","  print(i)\n","  print(gc_res)\n","print(\"Topic 5\")\n","for i in range (1,8):\n","  gc_res = grangercausalitytests(data[[\"GBPUSD\", \"International Trade News\"]], [i])\n","  print(i)\n","  print(gc_res)\n","# print(\"EPU_US\")\n","# for i in range (1,7):\n","#   gc_res = grangercausalitytests(data[[\"GBPUSD\", \"EPU_US\"]], [i])\n","#   print(i)\n","#   print(gc_res)\n","# print(\"EPU_UK\")\n","# for i in range (1,7):\n","#   gc_res = grangercausalitytests(data[[\"GBPUSD\", \"EPU_UK\"]], [i])\n","#   print(i)\n","#   print(gc_res)"],"metadata":{"id":"WZWxEf_RdaMP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# from statsmodels.tsa.stattools import grangercausalitytests\n","# maxlag=7 #becuase we got this value before. We are not suppose to add 1 to it\n","# test = 'ssr_chi2test'\n","# def grangers_causation_matrix(data, variables, test='ssr_chi2test', verbose=False):\n","#     \"\"\"Check Granger Causality of all possible combinations of the Time series.\n","#     The rows are the response variable, columns are predictors. The values in the table\n","#     are the P-Values. P-Values lesser than the significance level (0.05), implies\n","#     the Null Hypothesis that the coefficients of the corresponding past values is\n","#     zero, that is, the X does not cause Y can be rejected.\n","\n","#     data      : pandas dataframe containing the time series variables\n","#     variables : list containing names of the time series variables.\n","#     \"\"\"\n","#     df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)\n","#     for c in df.columns:\n","#         for r in df.index:\n","#             test_result = grangercausalitytests(data[[r, c]], maxlag=maxlag, verbose=False)\n","#             p_values = [round(test_result[i+1][0][test][1],4) for i in range(maxlag)]\n","#             if verbose: print(f'Y = {r}, X = {c}, P Values = {p_values}')\n","#             min_p_value = np.min(p_values)\n","#             df.loc[r, c] = min_p_value\n","#     df.columns = [var + '_x' for var in variables]\n","#     df.index = [var + '_y' for var in variables]\n","#     return df\n","# granger = grangers_causation_matrix(data, variables = data.columns)\n","# #grangers_causation_matrix(data, variables = data.columns)\n","# #print(granger.to_latex())"],"metadata":{"id":"zLJ56MHhemGW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data['allnews'] = data['Stock Market News'] + data['Economic Development News'] + data['FED News'] +data['Micro Finance News'] +data['International Trade News']\n","gc_res = grangercausalitytests(data[[\"allnews\", \"GBPUSD\"]],24)\n","gc_res\n","data = data.drop(\"allnews\", axis=1)\n","#FED News\n","#Micro Finance News\n","#International Trade News\n","gc_res"],"metadata":{"id":"B0TC6N8ze0zs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Stock Market News\"]],7)\n","#gc_res = grangercausalitytests(data[[\"Stock Market News\", \"GBPUSD\"]],7)\n","#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Economic Development News\"]],7)\n","#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"FED News\"]],7)\n","#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"Micro Finance News\"]],7)\n","#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"International Trade News\"]],7)\n","#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"EPU_US\"]],7)\n","#gc_res = grangercausalitytests(data[[\"GBPUSD\", \"EPU_UK\"]],7)\n","data['allnews'] = data['Stock Market News'] + data['Economic Development News'] + data['FED News'] +data['Micro Finance News'] +data['International Trade News']\n","gc_res = grangercausalitytests(data[[\"GBPUSD\", \"allnews\"]],24)\n","gc_res\n","data = data.drop(\"allnews\", axis=1)\n","#FED News\n","#Micro Finance News\n","#International Trade News\n","gc_res"],"metadata":{"id":"L9a8sknMfLMp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","gc_res = grangercausalitytests(data[[\"allnews\", \"GBPUSD\"]],7)\n","gc_res\n","data = data.drop(\"allnews\", axis=1)\n","#FED News\n","#Micro Finance News\n","#International Trade News\n","gc_res"],"metadata":{"id":"EYQ2GAswdv9g"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Instantaneous causality\n","import pandas as pd\n","from statsmodels.tsa.stattools import grangercausalitytests\n","\n","def check_instantaneous_causality(data, var1, var2, max_lag=1, significance_level=0.05):\n","    \"\"\"\n","    Perform Granger causality tests to check for instantaneous causality between two variables.\n","\n","    Parameters:\n","    - data: DataFrame containing the time series data.\n","    - var1: Name of the first variable.\n","    - var2: Name of the second variable.\n","    - max_lag: Maximum lag to consider in the Granger causality tests (default is 1 for instantaneous causality).\n","    - significance_level: Significance level for the Granger causality tests (default is 0.05).\n","\n","    Returns:\n","    - results: Dictionary containing the results of the Granger causality tests.\n","    \"\"\"\n","    # Extract the variables from the DataFrame\n","    series1 = data[var1]\n","    series2 = data[var2]\n","\n","    # Perform Granger causality tests\n","    results = grangercausalitytests(data[[var1, var2]], maxlag=1, verbose=False)\n","\n","    # Extract p-values from the test results\n","    p_values = [results[i+1][0]['ssr_ftest'][1] for i in range(max_lag)]\n","\n","    # Check for significance based on the specified significance level\n","    significant = any(p_value < significance_level for p_value in p_values)\n","\n","    # Print results\n","    print(f\"Granger causality test results for {var1} causing {var2}:\")\n","    for lag, p_value in enumerate(p_values, 1):\n","        print(f\"  Lag {lag}: p-value = {p_value}\")\n","\n","    # Print conclusion\n","    if significant:\n","        print(\"Conclusion: There is evidence of instantaneous causality.\")\n","    else:\n","        print(\"Conclusion: There is no evidence of instantaneous causality.\")\n","\n","    return results\n","\n","# Example usage:\n","# Assuming you have a DataFrame 'data' containing time series data with columns 'var1' and 'var2'\n","# and you want to test for instantaneous causality between 'var1' and 'var2' at lag 1\n","# results = check_instantaneous_causality(data, 'var1', 'var2', max_lag=1, significance_level=0.05)\n","check_instantaneous_causality(data, 'Stock Market News', 'GBPUSD')"],"metadata":{"id":"csRXGE08pznN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["check_instantaneous_causality(data, 'GBPUSD', 'FED News')"],"metadata":{"id":"xBDESQgbqjRw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# K-Fold Cross-Validation (Not necessary, as our main goal is not forecasting)"],"metadata":{"id":"V9qpOHvbJgz3"}},{"cell_type":"code","source":["from statsmodels.tsa.api import VAR\n","from sklearn.model_selection import TimeSeriesSplit\n","from sklearn.metrics import mean_squared_error\n","\n","# Assuming 'data' is your input dataframe with variables in columns and time points in rows\n","\n","# Define the number of folds for cross-validation\n","n_splits = 5\n","\n","# Initialize a TimeSeriesSplit object\n","tscv = TimeSeriesSplit(n_splits=n_splits)\n","\n","# Initialize lists to store evaluation metrics\n","rmse_list = []\n","\n","# Iterate through the cross-validation folds\n","for train_index, test_index in tscv.split(data):\n","    # Split the data into training and testing sets\n","    train_data, test_data = data.iloc[train_index], data.iloc[test_index]\n","\n","    # Fit the VAR model on the training set\n","    model = VAR(train_data)\n","    results = model.fit()\n","\n","    # Forecast the values for the testing set\n","    forecast = results.forecast(train_data.values[-results.k_ar:], len(test_data))\n","\n","    # Calculate RMSE for the forecast\n","    rmse = mean_squared_error(test_data, forecast, squared=False)\n","    rmse_list.append(rmse)\n","\n","# Calculate the mean RMSE across all folds\n","mean_rmse = np.mean(rmse_list)\n","\n","# Print the mean RMSE\n","print(\"Mean RMSE:\", mean_rmse)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J8RvFu4hJlFE","executionInfo":{"status":"ok","timestamp":1711362432766,"user_tz":-60,"elapsed":466,"user":{"displayName":"Digital 2020","userId":"05957751820960799131"}},"outputId":"28a754d7-e213-4c7b-cfee-4a845c422d13"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mean RMSE: 0.061269223859123\n"]}]},{"cell_type":"markdown","source":["Mean RMSE: 0.061269223859123"],"metadata":{"id":"SQMLMXOhOlze"}}],"metadata":{"colab":{"provenance":[{"file_id":"1Z-XClrKZwq5w46VYTzQ87igd6eBEX_Hw","timestamp":1675415518874}],"toc_visible":true,"collapsed_sections":["V9qpOHvbJgz3"]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}